Search

In this paper, an ensemble of clustering trees (ECTs) is adopted to improve the performance of the Fuzzy Min–Max (FMM) network with individual clustering trees. The key advantage of combining FMM and ECT together is to formulate an accurate and useful learning model that is able to perform online clustering and to explain its predictions. The online clustering capability is inherited from the FMM hyperboxes, while the explanatory capability arises from the underlying decision trees of ECT. Four different mean measures, namely harmonic, geometric, arithmetic, and root mean square, are incorporated into FMM for computing its hyperbox centroids. A series of benchmark and real-world data sets are used for evaluating the FMM-ECT performance. The results are analyzed and compared with those from other models. The outcomes indicate that FMM-ECT is able to achieve comparable clustering performances, with the advantage of providing explanations of its predictions using a decision tree. [ABSTRACT FROM AUTHOR]

Transcranial Doppler (TCD) is a reliable technique with the advantage of being non-invasive for the diagnosis of cerebrovascular diseases using blood flow velocity measurements pertaining to the cerebral arterial segments. In this study, the recurrent neural network (RNN) is used to classify TCD signals captured from the brain. A total of 35 real, anonymous patient records are collected, and a series of experiments for stenosis diagnosis is conducted. The extracted features from the TCD signals are used for classification using a number of RNN models with recurrent feedbacks. In addition to individual RNN results, an ensemble RNN model is formed in which the majority voting method is used to combine the individual RNN predictions into an integrated prediction. The results, which include the accuracy, sensitivity, and specificity rates as well as the area under the Receiver Operating Characteristic curve, are compared with those from the Random Forest Ensemble model. The outcome positively indicates the usefulness of the RNN ensemble as an effective method for detecting and classifying blood flow velocity changes due to brain diseases. [ABSTRACT FROM AUTHOR]

In this paper, two real-world medical classification problems using electrocardiogram (ECG) and auscultatory blood pressure (Korotkoff) signals are examined. A total of nine machine learning models are applied to perform classification of the medical data sets. A number of useful performance metrics which include accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve are computed. In addition to the original data sets, noisy data sets are generated to evaluate the robustness of the classifiers against noise. The 10-fold cross validation method is used to compute the performance statistics, in order to ensure statistically reliable results pertaining to classification of the ECG and Korotkoff signals are produced. The outcomes indicate that while logistic regression models perform the best with the original data set, ensemble machine learning models achieve good accuracy rates with noisy data sets. [ABSTRACT FROM AUTHOR]

Training neural networks in distinguishing different emotions from physiological signals frequently involves fuzzy definitions of each affective state. In addition, manual design of classification tasks often uses sub-optimum classifier parameter settings, leading to average classification performance. In this study, an attempt to create a framework for multi-layered optimization of an ensemble of classifiers to maximize the system's ability to learn and classify affect, and to minimize human involvement in setting optimum parameters for the classification system is proposed. Using fuzzy adaptive resonance theory mapping (ARTMAP) as the classifier template, genetic algorithms (GAs) were employed to perform exhaustive search for the best combination of parameter settings for individual classifier performance. Speciation was implemented using subset selection of classification data attributes, as well as using an island model genetic algorithms method. Subsequently, the generated population of optimum classifier configurations was used as candidates to form an ensemble of classifiers. Another set of GAs were used to search for the combination of classifiers that would result in the best classification ensemble accuracy. The proposed methodology was tested using two affective data sets and was able to produce relatively small ensembles of fuzzy ARTMAPs with excellent affect recognition accuracy. [ABSTRACT FROM AUTHOR]

Highlights: [•] An ensemble of hybrid intelligent models is applied to condition monitoring tasks. [•] A review on condition monitoring of induction motors is presented. [•] The proposed model is able to learn incrementally and explain its predictions. [•] The results ascertain the usefulness of the model for condition monitoring tasks. [•] The knowledge base is presented as a decision tree for interpretation by users. [ABSTRACT FROM AUTHOR]

Highlights: [•] A hybrid intelligent system is proposed for medical data classification tasks. [•] The proposed system is able to learn incrementally and explain its predictions. [•] Benchmark medical data sets are used to evaluate the effectiveness of the system. [•] The results ascertain the usefulness of the system for medical decision support. [•] The knowledge base is presented as a decision tree for interpretation by users. [ABSTRACT FROM AUTHOR]

A novel comprehensive methodology integrated with genetic algorithm (GA) has been formulated by accounting for local spectral irradiance and the specifications of commercial photovoltaic (PV) module. Despite having the same solar irradiance, variation in power conversion efficiency (PCE) of PV module can be significant in different site locations by referencing to that of standard AM1.5 spectrum. We have carried out a case study using GA for a combination of three site locations in Peninsular Malaysia and twelve types of commercial PV modules. Type-9 PV module operating in Bangi has recorded the lowest gain with 0.1%, while type-10 PV module operating in Bandar Sungai Long has shown the best gain of up to 27%. For solving multi-objective problems, MmGA and NSGAII have been applied to optimize three objectives concurrently including PCE, PV weight and PV panel area. From the simulation, type-1, type-6, and type-12 PV modules show the best in at least two objectives for the categories of m-Si, p-Si, and thin film, respectively. [Display omitted] • A comprehensive methodology has been formulated using genetic algorithm. • Type-9 PV module operating in Bangi has the lowest gain with 0.1%. • Type-10 PV module operating in Bandar Sungai Long has the best gain with 27%. • Type-7 PV module shows the highest increment 27.5% in annual energy yield. • Type-1, type-6 & type-12 PV modules are optimized for m-Si, p-Si & thin film. [ABSTRACT FROM AUTHOR]

The decision making process in communication is affected by internal and external factors from dynamic environments. Humans can perform a variety of behaviors in a similar situation, unlike robots. This paper discusses human psychological phenomena during communication from the point of view of internal and external factors, such as perception, memory, and emotional information. Based on these, we introduce the personality affected robotic emotional model and the emotion affected associative memory model for the robot. We organize an interactive robot system to provide suitable decisions for the robot. Results from interactive communication experiments indicate that the robot is able to perform different actions based on internal and external factors. [ABSTRACT FROM AUTHOR]

Highlights • A hybrid model (FMM-BSO) for feature selection and data classification is proposed. • Fuzzy Min–Max (FMM) is first used for incremental learning of data. • A Brain Storm Optimization (BSO) is then used for feature selection. • The outcome indicates that FMM-BSO is able to produce promising results. Abstract Swarm intelligence (SI)-based optimization methods have been extensively used to tackle feature selection problems. A feature selection method extracts the most significant features and removes irrelevant ones from the data set, in order to reduce feature dimensionality and improve the classification accuracy. This paper combines the incremental learning Fuzzy Min–Max (FMM) neural network and Brain Storm Optimization (BSO) to undertake feature selection and classification problems. Firstly, FMM is used to create a number of hyperboxes incrementally. BSO, which is inspired by the human brainstorming process, is then employed to search for an optimal feature subset. Ten benchmark problems and a real-world case study are conducted to evaluate the effectiveness of the proposed FMM-BSO. In addition, the bootstrap method with the 95% confidence intervals is used to quantify the results statistically. The experimental results indicate that FMM-BSO is able to produce promising results as compared with those from the original FMM network and other state-of-the-art feature selection methods such as particle swarm optimization, genetic algorithm, and ant lion optimization. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-step prediction model for time series prediction, i.e. Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine with Drift Detector Mechanism (Meta-RKOS-ELM ALD). Recurrent multi-step algorithm is applied to release the limitation in the number of prediction steps, and Drift Detector Mechanism (DDM) is used to overcome the problem of concept drift in the prediction model. The new meta-cognitive strategy decides the way of the incoming data during training, which decreases the training computation of prediction model and solves the parameter dependency. In our evaluation, we use a total of six artificial data sets and three real-world data sets (Standard & Poor's 500 Index, Shanghai Stock Exchange Composite Index, and Ozone Concentration in Toronto) to prove the ability of kernel filters, the detecting ability of concept drift detector, and situation of applying meta-cognitive strategy in our proposed model. Experiments results indicate that the Meta-KOS-ELM ALD with DDM has better forecasting ability in various predicting periods with the shortest learning time, as compared with other algorithms. [ABSTRACT FROM AUTHOR]