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It is meaningful to create a comprehensive score to extract information from mass continuous data when they measure the same latent concept. Therefore, this study adopts the logic of psychometrics to conduct scales on continuous data under the Rasch models. This study also explores the effect of different data discretization methods on scale development by using financial profitability ratios as a demonstration. Results show that retaining more categories can benefit Rasch modeling because it can better inform the models. The dynamic clustering algorithm, k-median is a better method for extracting characteristic patterns of the continuous data and preparing the data for the Rasch model. This study illustrates that there is no one-way good discretization method for continuous data under the Rasch models. It is more reasonable to use the traditional algorithms if each continuous data variable has target benchmark(s), whereas the k-median clustering algorithm achieves good modeling results when benchmark information is lacking. [ABSTRACT FROM AUTHOR]

Meteorological data are useful in many fields related to climate change studies and their use often requires them to be continuous. To date, marginal distribution sampling (MDS), which consists of filling a missing value with an average of the data that are found in similar meteorological conditions over a flexible time window, is widely adopted in the FLUXNET community. In this work, we evaluate the performance of MDS at diurnal and monthly scales for the incoming shortwave radiation (Swin), relative humidity (RH), vapour pressure deficit (VPD), air and soil temperatures (Tair, Tsoil) acquired across seven sites in West Africa. The criteria tested are the MDS's ability to (i) fill gaps while reducing the error rate, (ii) represent proper variability within data and finally (iii) ensure homogeneity between its output and original data. We found during the daytime that MDS is adequate for filling gaps in Swin when both reducing error rate and a good representation of variability are targeted. If the goal is to have a small error rate, then this approach is recommended for all investigated variables except VPD. During nighttime, MDS is satisfactory to minimize the error when filling gaps in Tair, Tsoil and RH while to represent their variabilities it becomes more sensitive to the rate of missing data. At a monthly scale, the gap‐filled data are consistent with the original ones for all variables attributable to data size and a wider sliding window that allows more data under similar conditions to be considered. [ABSTRACT FROM AUTHOR]

Abstract Meteorological data are useful in many fields related to climate change studies and their use often requires them to be continuous. To date, marginal distribution sampling (MDS), which consists of filling a missing value with an average of the data that are found in similar meteorological conditions over a flexible time window, is widely adopted in the FLUXNET community. In this work, we evaluate the performance of MDS at diurnal and monthly scales for the incoming shortwave radiation (Swin), relative humidity (RH), vapour pressure deficit (VPD), air and soil temperatures (Tair, Tsoil) acquired across seven sites in West Africa. The criteria tested are the MDS's ability to (i) fill gaps while reducing the error rate, (ii) represent proper variability within data and finally (iii) ensure homogeneity between its output and original data. We found during the daytime that MDS is adequate for filling gaps in Swin when both reducing error rate and a good representation of variability are targeted. If the goal is to have a small error rate, then this approach is recommended for all investigated variables except VPD. During nighttime, MDS is satisfactory to minimize the error when filling gaps in Tair, Tsoil and RH while to represent their variabilities it becomes more sensitive to the rate of missing data. At a monthly scale, the gap‐filled data are consistent with the original ones for all variables attributable to data size and a wider sliding window that allows more data under similar conditions to be considered.

We examined the performance of coefficient alpha and its potential competitors (ordinal alpha, omega total, Revelle's omega total [omega RT], omega hierarchical [omega h], greatest lower bound [GLB], and coefficient H) with continuous and discrete data having different types of non-normality. Results showed the estimation bias was acceptable for continuous data with varying degrees of non-normality when the scales were strong (high loadings). This bias, however, became quite large with moderate strength scales and increased with increasing non-normality. For Likert-type scales, other than omega h, most indices were acceptable with non-normal data having at least four points, and more points were better. For different exponential distributed data, omega RT and GLB were robust, whereas the bias of other indices for binomial-beta distribution was generally large. An examination of an authentic large-scale international survey suggested that its items were at worst moderately non-normal; hence, non-normality was not a big concern. We recommend (a) the demand for continuous and normally distributed data for alpha may not be necessary for less severely non-normal data; (b) for severely non-normal data, we should have at least four scale points, and more points are better; and (c) there is no single golden standard for all data types, other issues such as scale loading, model structure, or scale length are also important. [ABSTRACT FROM AUTHOR]

In computing statistical measures from a frequency table, the midpoint represents the observations in each class. The assumption that the observations are distributed uniformly and concentrated around the midpoint is not always valid. In this article, two new methods of constructing a frequency table, using the mean and median of the classes, are compared with the existing method, which uses the midpoint. The simulation study using symmetrical positive data shows that the method which used the classes’ mean performed well compared with the other methods. Freedman and Diaconis (FD) rule is the recommended rule when choosing the number of classes. [ABSTRACT FROM AUTHOR]

We examined continuous affect drawings as innovative measure of affective experiences over time. Intensive longitudinal data often rely on discrete assessments, containing “blind spots” between measurements. With continuous affect drawings participants visually depict their affect fluctuations between assessments. In an experience sampling study, participants (N = 115) rated their momentary positive and negative affect 6 times daily. From the second daily rating on, they additionally drew their positive and negative affect changes and reported affective events between assessments. They received one measurement burst between assessments daily. The strength of the approach is a substantial amount of informational gain (average 7%) over linearly interpolated points between assessments. The additional information was subsequently categorized into positive and negative affect peaks and valleys, each occurring once a day per person on average. The probability of detecting peaks and valleys increased with reported events. The drawings correlated positively with momentary affect scores from the burst. Yet, the drawing predicted the bursts less well suggesting that the momentary ratings may yield different information than the drawings. Although the timing of retrospective drawings is less precise than individual momentary assessments, this method provides a comprehensive understanding of affective experiences between assessments, offering a unique perspective on affect dynamics. [ABSTRACT FROM AUTHOR]

The representation of a land cover type (i.e. habitat) within an area is often used as an explanatory variable in species distribution models. However, it is possible that a simple binary presence/absence of the suitable habitat might be the most important determinant of the presence/absence of some species and, thus, be a better predictor of species occurrence than the continuous parameter (area). We hypothesize that the binary predictor is more suitable for relatively rare habitats (e.g. wetlands) while for common habitats (e.g. forests) the amount of the focal habitat is a better predictor. We used the Third Atlas of Breeding Birds in the Czech Republic as the source of species distribution data and CORINE Land Cover inventory as the source of the landcover information. To test our hypothesis, we fitted generalized linear models of 32 water and 32 forest bird species. Our results show that for water bird species, models using binary predictors (presence/absence of the habitat) performed better than models with continuous predictors (i.e. the amount of the habitat); for forest species, however, we observed the opposite. Thus, future studies using habitats as predictors of species occurrences should consider the prevalence of the habitat in the landscape, and the biological role of the habitat type in the particular species' life history. In addition, performing a preliminary comparison of the performance of the binary and continuous versions of habitat predictors (e.g. using information criteria) prior to modelling, during variable selection, can be beneficial. These are simple steps that will improve explanatory and predictive performance of models of species distributions in biogeography, community ecology, macroecology and ecological conservation. [ABSTRACT FROM AUTHOR]

Abstract Background Randomised controlled trials (RCTs) provide valuable information for developing harm profiles but current analysis practices to detect between-group differences are suboptimal. Drug trials routinely screen continuous clinical and biological data to monitor participant harm. These outcomes are regularly dichotomised into abnormal/normal values for analysis. Despite the simplicity gained for clinical interpretation, it is well established that dichotomising outcomes results in a considerable reduction in information and thus statistical power. We propose an automated procedure for the routine implementation of the distributional method for the dichotomisation of continuous outcomes proposed by Peacock and Sauzet, which retains the precision of the comparison of means. Methods We explored the use of a distributional approach to compare differences in proportions based on the comparison of means which retains the power of the latter. We applied this approach to the screening of clinical and biological data as a means to detect ‘signals’ for potential adverse drug reactions (ADRs). Signals can then be followed-up in further confirmatory studies. Three distributional methods suitable for different types of distributions are described. We propose the use of an automated approach using the observed data to select the most appropriate distribution as an analysis strategy in a RCT setting for multiple continuous outcomes. We illustrate this approach using data from three RCTs assessing the efficacy of mepolizumab in asthma or COPD. Published reference ranges were used to define the proportions of participants with abnormal values for a subset of 10 blood tests. The between-group distributional and empirical differences in proportions were estimated for each blood test and compared. Results Within trials, the distributions varied across the 10 outcomes demonstrating value in a practical approach to selecting the distributional method in the context of multiple adverse event outcomes. Across trials, there were three outcomes where the method chosen by the automated procedure varied for the same outcome. The distributional approach identified three signals (eosinophils, haematocrit, and haemoglobin) compared to only one when using the Fisher’s exact test (eosinophils) and two identified by use of the 95% confidence interval for the difference in proportions (eosinophils and potassium). Conclusion When dichotomisation of continuous adverse event outcomes aids clinical interpretation, we advocate use of a distributional approach to retain statistical power. Methods are now easy to implement. Retaining information is especially valuable in the context of the analysis of adverse events in RCTs. The routine implementation of this automated approach requires further evaluation.

Many industrial sectors have been collecting big sensor data. With recent technologies for processing big data, companies can exploit this for automatic failure detection and prevention. We propose the first completely automated method for failure analysis, machine-learning fault trees from raw observational data with continuous variables. Our method scales well and is tested on a real-world, five-year dataset of domestic heater operations in The Netherlands, with 31 million unique heater-day readings, each containing 27 sensor and 11 failure variables. Our method builds on two previous procedures: the C4.5 decision-tree learning algorithm, and the LIFT fault tree learning algorithm from Boolean data. C4.5 pre-processes each continuous variable: it learns an optimal numerical threshold which distinguishes between faulty and normal operation of the top-level system. These thresholds discretise the variables, thus allowing LIFT to learn fault trees which model the root failure mechanisms of the system and are explainable. We obtain fault trees for the 11 failure variables, and evaluate them in two ways: quantitatively, with a significance score, and qualitatively, with domain specialists. Some of the fault trees learnt have almost maximum significance (above 0.95), while others have medium-to-low significance (around 0.30), reflecting the difficulty of learning from big, noisy, real-world sensor data. The domain specialists confirm that the fault trees model meaningful relationships among the variables.

Aiming at the problems of information loss in continuous data processing, inconsistent information introduced by granulation strategies, and difficulty in parameter optimization in the existing rough set attribute reduction methods, this paper proposed a non-monotonic heuristic attribute reduction algorithm based on category discrimination which was suitable for continuous data. Firstly, it divided the universe according to the label of each sample, and combined the samples of the same label into a cluster, and defined the inter-class discrimination and intra-class discrimination of each cluster. Secondly, it defined a new attribute importance criterion to determine the optimal reduction set, so as to improve the classification performance of subsequent classifiers. The proposed algorithm was compared with other 6 attribute reduction algorithms on the 11 UCI datasets. The results show that compared with the 6 algorithms, the average dimension of the reduction sets obtained by the proposed algorithm is reduced by 1. 16, and the average classification accuracy is improved by 3. 42%, showing better reduction performance. [ABSTRACT FROM AUTHOR]

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization. [ABSTRACT FROM AUTHOR]

The article presents a novel weak ground truth (WGT) determination procedure on continuous human-rated data. The notion of WGT is essential in cases where there is no direct empirical evidence for an observed construct and human annotations provide the most reliable means for determining the ground truth. The core idea behind the proposed procedure is to transform the ratings to reduce rater bias, maximize inter-rater agreement, and improve WGT. The procedure was evaluated on two behavioral datasets containing continuous annotations of several expressive dimensions. The results show that the procedure improves the size of WGT data by removing the disagreement originating from rater-specific distortions, such as rater mean and scaling bias. The entropy of residuals decreases after WGT optimization, meaning that more relevant information is retained. The average improvement of WGT data size is between 10.1 and 20.9 percentage points, depending on the respective dimension. However, in cases where the rater bias is small, the procedure does not substantially modify WGT. This indicates that the proposed optimization only removes rater biases derived from rater-specific distortions, while retaining and improving valid WGT. The proposed procedure is generalizable on any type and size of continuous or discrete numerical data where multiple raters are involved.

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

With the considerable exploration of blockchain in various industrial fields, the storage architectures of mainstream consortium blockchains exhibit significant performance limitations, which can't meet the requirements of efficient data access with massive data storage in enterprise-level business scenarios. In this paper, we creatively divided the underlying data of the consortium blockchain into two categories: continuous data and state data and proposed a new storage architecture to store and operate these two types of data efficiently. For continuous data, we designed a specialized index-based storage engine. For state data, we proposed a multi-level cache mechanism with a secure and integrated data persistence policy. In addition, a pluggable Client/Server mode is employed to achieve flexible distributed extension. A series of experiments are conducted to show the effectiveness of our architecture. Compared with mainstream consortium blockchain storage architecture based on LevelDB, the average time-consuming decreases 81.85%/82.47% for reading/writing continuous data and 22.21%/48.99% for reading/writing state data. Compared with the storage architecture based on distributed database TiKV, the time-consuming decreases more significantly. This storage architecture has been integrated into the enterprise-level consortium blockchain platform Hyperchain, which has supported the efficient running of dozens of large-scale commercial blockchain projects with massive data.

Background: Randomised controlled trials (RCTs) provide valuable information for developing harm profiles but current analysis practices to detect between-group differences are suboptimal. Drug trials routinely screen continuous clinical and biological data to monitor participant harm. These outcomes are regularly dichotomised into abnormal/normal values for analysis. Despite the simplicity gained for clinical interpretation, it is well established that dichotomising outcomes results in a considerable reduction in information and thus statistical power. We propose an automated procedure for the routine implementation of the distributional method for the dichotomisation of continuous outcomes proposed by Peacock and Sauzet, which retains the precision of the comparison of means.Methods: We explored the use of a distributional approach to compare differences in proportions based on the comparison of means which retains the power of the latter. We applied this approach to the screening of clinical and biological data as a means to detect 'signals' for potential adverse drug reactions (ADRs). Signals can then be followed-up in further confirmatory studies. Three distributional methods suitable for different types of distributions are described. We propose the use of an automated approach using the observed data to select the most appropriate distribution as an analysis strategy in a RCT setting for multiple continuous outcomes. We illustrate this approach using data from three RCTs assessing the efficacy of mepolizumab in asthma or COPD. Published reference ranges were used to define the proportions of participants with abnormal values for a subset of 10 blood tests. The between-group distributional and empirical differences in proportions were estimated for each blood test and compared.Results: Within trials, the distributions varied across the 10 outcomes demonstrating value in a practical approach to selecting the distributional method in the context of multiple adverse event outcomes. Across trials, there were three outcomes where the method chosen by the automated procedure varied for the same outcome. The distributional approach identified three signals (eosinophils, haematocrit, and haemoglobin) compared to only one when using the Fisher's exact test (eosinophils) and two identified by use of the 95% confidence interval for the difference in proportions (eosinophils and potassium).Conclusion: When dichotomisation of continuous adverse event outcomes aids clinical interpretation, we advocate use of a distributional approach to retain statistical power. Methods are now easy to implement. Retaining information is especially valuable in the context of the analysis of adverse events in RCTs. The routine implementation of this automated approach requires further evaluation. [ABSTRACT FROM AUTHOR]

In diagnostic accuracy studies, a commonly employed approach involves dichotomizing continuous data and subsequently analyzing them using a Bayesian latent class model (BLCM), often relying on binomial or multinomial distributions, rather than preserving their continuous nature. However, this procedure can inadvertently lead to less reliable outcomes due to the inherent loss of information when converting the original continuous measurements into binary values. Through comprehensive simulations, we demonstrated the limitations and disadvantages of dichotomizing continuous biomarkers from two correlated tests. Our findings highlighted notable disparities between the true values and the model estimates as a result of dichotomization. We discovered the crucial significance of selecting a reference test with high diagnostic accuracy in test evaluation in order to obtain reliable estimates of test accuracy and prevalences. Our study served as a call to action for veterinary researchers to exercise caution when utilizing dichotomization. [ABSTRACT FROM AUTHOR]

Neighborhood rough set theories are commonly used in global feature selection to achieve high performance in continuous data classification. However, selecting a single feature subset to represent the entire dataset may degrade the performance when there are intra-class dissimilarities among objects. Therefore, this paper proposes a novel feature-selection method, Granule-specific Feature Selection (GFS) to select local feature subsets for continuous data classification. The feature selection approach constitutes a novel feature selection algorithm and a novel feature evaluation function and uses existing approaches for granule identification and classification with some adjustments. The neighborhood rough set theories are used in granule (subclass) identification within each class when there are no subclass label information available in the training data, while an improved k-Nearest Neighbors approach is used in classification with granule-specific feature subsets. Experimental results show GFS outperforms most of the global, class-specific, and local feature selection baselines in terms of classification performance. • Granule-specific feature selection using neighborhood rough set theories. • Feature selection while managing intra-class dissimilarities of objects. • Handle data uncertainty of continuous data when selecting features. • Improve classification performance using granule-specific feature subsets. [ABSTRACT FROM AUTHOR]

Outlier detection is extensively adopted in machine learning applications to identify rare but significant objects in a data distribution that deviate from the majority of objects. However, none of the existing outlier definitions use the available contextual label information in a dataset to improve the identification of outliers, though it could improve the performance of data analysis. In this study, we propose a novel definition for outliers considering the available contextual label information along with a method to remove outliers based on the proposed definition to improve classification performance and granulation purity. The experimental results on eight public datasets show that the removal of outliers using the novel method highly improves the classification accuracy with Support Vector Machine, k-Nearest Neighbors, and Classification and Regression Tree algorithms while identifying 100% pure subclasses of the labeled major classes. This method is beneficial in identifying the outliers automatically when the data contains classification information about a specific application instead of outlierness information. • Outlier removal approach to improve the performance of the remaining data. • Define outliers using the available contextual label information. • Identify outliers which reduce the purity of data granules within a dataset. • Confirm 100% purity of the granules (clusters) after the outlier removal. [ABSTRACT FROM AUTHOR]

There are inconsistencies between the formulas for the variance of standardized mean difference (SMD) in the Cochrane Handbook for Systematic Reviews and the variance reported in other sources. Instead of the variance appropriate for the SMD of a crossover experiment, the Cochrane Handbook uses the variance appropriate for a pre-test post-test experiment. This means that if there is a non-negligible time period effect, the formula reported by the Handbook will underestimate both the effect size and its variance. In addition, the formula for the standard error of SMD reported in the Cochrane Handbook (in section 23.2.7.2) is inconsistent with the variance derived from the variance of the related t-test. Even if the period effect is negligible, the Cochrane Handbook formula is biased toward underestimates. The difference between the estimates from the two formulas will be small if either the correlation between the repeated measures, or the magnitude of the SMD estimate, is small, or if the sample size is large. However, it can be can be quite substantial in other circumstances. [ABSTRACT FROM AUTHOR]

In the paper, a process capability and process performance analyses for continuous and attribute data are conducted by using an industry example. The inner diameter of the bearing rings is analysed by applying the normal capability analysis for the continuous data and by applying the binomial capability analysis for the attribute data. In order to quantify the process performance and process capability for continuous and attribute data, the sigma level or the process sigma is calculated. This represents an alternative method to calculating process capability indices and process performance indices.

Natural disasters like earthquakes and heavy rainfall events induce slope failures and other disasters. Proper determination of the water retention properties of porous mediums is necessary. The conventional Soil Water Characteristics Curve (SWCC) determination techniques are limited due to their complexity and lengthy time required. A highly accurate simple remolded and undisturbed samples SWCC determination method that requires a short time is lacking. A novel automatic system which adopts the continuous pressurization method and allows the direct determination of the SWCC in a short time suitable for use with remolded and undisturbed samples was developed. The accuracy of the newly developed system was validated, and it was shown to have excellent repeatability. It is a reliable and direct method that requires a short time for the SWCC determination, with the drying and wetting SWCCs obtainable in less than 10% of the time required using the conventional multi-step flow method. Consideration of the pore water pressure was shown to be necessary when calculating the matric suction utilizing the axis-translation technique. Finally, because remolded samples do not properly represent the in-situ natural conditions, the significant discrepancies should be carefully considered when conducting analysis and proposing countermeasures against unsaturated soil-related Geo-disasters. [ABSTRACT FROM AUTHOR]

In many applications of mobile crowd sensing, participants should collect continuous data over a period of time in this scenario on which existing research lacks consideration. For this scenario, this paper proposed a participant selection mechanism which was time window dependent. This mechanism included a participant selection method which was time window dependent based on dynamic programming algorithm. The target of the method was to maximize data benefits while cover time period of the task. It gave an updating mechanism of participant's reputation based on the willingness and data quality of the participant. Finally, simulation results show that compared with two common selection mechanisms, the proposed participant selection mechanism has better performance in terms of data reliability, data benefits and cost and has better prospects. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we present new Poisson-type inequalities for Poisson integrals with continuous data on the boundary. The obtained inequalities are used to obtain growth properties at infinity of positive superharmonic functions in a smooth cone.

Reports highlighting the problems with the standard practice of using bar graphs to show continuous data have prompted many journals to adopt new visualization policies. These policies encourage authors to avoid bar graphs and use graphics that show the data distribution; however, they provide little guidance on how to effectively display data. We conducted a systematic review of studies published in top peripheral vascular disease journals to determine what types of figures are used, and to assess the prevalence of suboptimal data visualization practices. Among papers with data figures, 47.7% of papers used bar graphs to present continuous data. This primer provides a detailed overview of strategies for addressing this issue by (1) outlining strategies for selecting the correct type of figure depending on the study design, sample size, and the type of variable; (2) examining techniques for making effective dot plots, box plots, and violin plots; and (3) illustrating how to avoid sending mixed messages by aligning the figure structure with the study design and statistical analysis. We also present solutions to other common problems identified in the systematic review. Resources include a list of free tools and templates that authors can use to create more informative figures and an online simulator that illustrates why summary statistics are meaningful only when there are enough data to summarize. Last, we consider steps that investigators can take to improve figures in the scientific literature. [ABSTRACT FROM AUTHOR]

The standardized mean difference is a well‐known effect size measure for continuous, normally distributed data. In this paper we present a general basis for important other distribution families. As a general concept, usable for every distribution family, we introduce the relative effect, also called Mann–Whitney effect size measure of stochastic superiority. This measure is a truly robust measure, needing no assumptions about a distribution family. It is thus the preferred tool for assumption‐free, confirmatory studies. For normal distribution shift, proportional odds, and proportional hazards, we show how to derive many global values such as risk difference average, risk difference extremum, and odds ratio extremum. We demonstrate that the well‐known benchmark values of Cohen with respect to group differences—small, medium, large—can be translated easily into corresponding Mann–Whitney values. From these, we get benchmarks for parameters of other distribution families. Furthermore, it is shown that local measures based on binary data (2 × 2 tables) can be associated with the Mann–Whitney measure: The concept of stochastic superiority can always be used. It is a general statistical value in every distribution family. It therefore yields a procedure for standardizing the assessment of effect size measures. We look at the aspect of relevance of an effect size and—introducing confidence intervals—present some examples for use in statistical practice. [ABSTRACT FROM AUTHOR]

The arrival of large voluminous continuous queries sets for a given query leads an insignificant insights. The elimination of certain data tuples occurs in order to balance the system load. The streaming query removes the improper data tuples and uses proper data tuples in the form of defined tables or sets. However, major drawback occurs due to unbounded streaming and inadequate access to end data. Due to such constraints, many stream processing methods makes the processed data unavailable for any applications or to the related queries of neighborhood branches. This paper avoids such problems during the process of data tuples at the generation of queries. The study uses a streaming model that executes effective query plans in continuous data. The streaming model aims reduce the communication cost and improves the scalability of continuous aggregation queries. It sub-divides the client query and executes it over data aggregators within the incoherent limit. A weighted dominant query algorithm is formulated to provide the top dominant value in each sub-query clusters. This reduces the cost for computation in synthetic databases. The experimental results proved that the proposed model with weighted dominant query algorithm effectively improves scalability by reducing the computational cost. [ABSTRACT FROM AUTHOR]

In this paper, a data-driven fault diagnosis model dealing with chemical imbalanced data streams is investigated. Different faults occur with varied frequencies by continuous arrival in chemical plants, while this issue has been hardly addressed in developing a diagnosis model. A novel incremental imbalance modified deep neural network (incremental-IMDNN) is proposed to promote the fault diagnosis to the imbalanced data stream. The first step in designing the incremental-IMDNN is the employment of an imbalance modified method combined with active learning for the extraction and generation of the most valuable information keeping in view the model feedback. DNN is utilized as a basic diagnosis model to excavate potential information. Then for the continuous arrival of new fault modes, DNN is promoted in an incremental hierarchical way. Unlike the traditional model that trained on a static snapshot of data, this model inherits the existing knowledge and hierarchically expands the diagnosis model by the similarity of faults. Similar faults that are judged by fuzzy clustering merge into a superclass, and every submodel shares the same architecture that is prevalent in previous research, which can be trained in parallel. We validate the performance of the proposed method in a Tennessee Eastman (TE) dataset, and the simulation results indicate that the proposed incremental-IM-DNN is better than the existing methods and possesses significant robustness and adaptability in chemical fault diagnosis. [ABSTRACT FROM AUTHOR]

Rough set theories are utilized in class-specific feature selection to improve the classification performance of continuous data while handling data uncertainty. However, most of those approaches are converting continuous data into discrete or fuzzy data before applying rough set theories. These data conversions can reduce or change the meaning of data, as well as introduce unnecessary complexity to the feature domain. Therefore, in this study, we use neighborhood rough sets in class-specific feature selection to improve the classification performance without data conversions. As the standard classification algorithms are capable of handling a single feature set, we propose a novel classification algorithm based on the K-Nearest Neighbour algorithm to use class-specific feature subsets. Experimental evaluations prove that the proposed approach outperforms most of the baselines, and the selected feature sets are more effective than using the full feature sets in classification. The approach highly reduces the number of selected features and hence, can be used for effective data analysis of continuous data with high performance. • Class-specific feature selection to improve continuous data classification. • Feature selection in continuous data while handling data uncertainty. • Control information losses that can occur through data conversions. • Neighborhood rough sets in class-specific feature selection without data conversions. • Improve K-nearest neighbor classification algorithm to use multiple feature subsets. [ABSTRACT FROM AUTHOR]