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The ultimate goal of listed companies is to maximize shareholders' wealth. With the increasingly fierce market competition, enterprise managers are constantly exploring the key indicators that have an important impact on the financial performance (FP) of enterprises, and achieve the expected FP of shareholders by improving these key indicators. On the basis of the existing enterprise performance measurement system and index research, through expert scoring to determine the secondary indicators, this paper selects 87 small and medium-sized board listed companies which officially announced the implementation of equity incentive from 2009 to 2012 as the sample, takes the financial information disclosed in 2013 as the empirical data, and analyzes the traditional multiple linear regression analysis (MLR) When dealing with big data, especially the data with hierarchical structure, this paper proposes a partial regression coefficient calculation model based on hierarchical data, constructs a multiple nonlinear regression model, and concludes through empirical analysis that there is a nonlinear correlation between equity incentive ratio and corporate performance, and that there is an interval effect between equity incentive ratio and corporate performance. We also present Fuzzy based financial performance prediction of listed companies. Finally, we demonstrate Comparative analysis for financial prediction in term of accuracy between multiple regression model and fuzzy logic system and result deduce that fuzzy logic gives better accuracy than regression model. [ABSTRACT FROM AUTHOR]

At present, China has great difficulty in obtaining the reliability of teaching data sources. In order to further improve the effectiveness of data mining and reduce the difficulty of data acquisition, this paper studies the design and simulation of integrated education information teaching system based on fuzzy logic. Bayesian algorithm can perform data mining, feature recognition and classification on data in big data, so that it can effectively process massive data sources. By weighting the different network structures, the number of undirected edges in the network is reduced, and then small data sets that can be processed by multiple traditional algorithms are sampled from the big data set, and data is generated by using the Bayesian network toolkit Samiam. The modules respectively generate data sets of different sizes and construct a teaching data source generation model. The experimental results show that RSEM on Child and Alarm data can take less time and achieve an accuracy of 86.17% compared with the whole data set under the same effect. This paper proposes a Bayesian network structure integration model, which can solve the problem of data acquisition difficulties, and is also a further improvement of data mining technology. [ABSTRACT FROM AUTHOR]

In this paper, a novel Feature-Reduction Fuzzy C-means (FRFCM) with Feature Linkage Weight (FRFCM-FLW) algorithm is introduced. By the combination of FRFCM and feature linkage weight, a new feature selection model is developed, called a Feature Linkage Weight Based FRFCM using fuzzy clustering. The larger amounts of features are superior to the complication of the problem, and the larger the time that is exhausted in creating the outcome of the classifier or the model. Feature selection has been established as a high-quality method for preferring features that best describes the data under certain criteria or measure. The proposed method presents three stages namely, 1) Data Formation: The process of data collection and data cleaning; 2) FRFCM-FLW. The proposed method can decrease feature elements routinely, and also construct excellent clustering results. The proposed method calculates a novel weight for every feature by combining modified Mahalanobis distance with feature δm variance in FRFCM algorithm; 3) Fuzzy C-means (FCM) cluster. The proposed FRFCM-FLW method proves high Accuracy Rate (AR), Rand Index (RI) and Jaccard Index (JI) ratio when compared to other feature reduction algorithms like WFCM, EWKM, WKM, FCM and FRFCM algorithms. [ABSTRACT FROM AUTHOR]

There are a lot of applications of multi-agent systems, such as robot navigation, distributed control, data mining, etc. Reinforcement learning (RL) is a popular method used in multi agent path planning. RL algorithm needs an accurate representation of a small and discrete space. In order to plan multi agents in continuous time, this paper approximate the Q-values with the fuzzy logic, such that the modified RL canwork in continuous state space. The fuzzy reinforcement learning proposed in this paper uses fuzzy Q-iteration algorithm and a modified Wolf-PH algorithm. The convergence and existence of the algorithm are proven. The continuous time planning algorithm is applied to a cooperative task of two mobile Khepera robots. The experimental results show the effectiveness of the new path planning method for the multi agents in continuous time. [ABSTRACT FROM AUTHOR]

With the rapid development of database and Internet technologies, data collection and storage is possible. It is often impossible to correctly analyze the valuable information contained in the data, and it becomes more difficult to obtain valuable information. Therefore, it faces the status of "rich data and scarce knowledge". Traditional information processing technology can no longer meet the needs of reality. There is an urgent need for more capable and effective information processing skills to help us analyze the information we need from big data and guide us to make the right decisions. Data mining technology is born in the background. Data mining technology is one of the effective methods to solve rich data and improve lack of knowledge. It is also one of the main research topics in the field of information science. Related research and applications have greatly improved people's decision-making ability. It has been recognized as one of the extremes of data research and has a very broad application prospect. Large databases often contain redundant and unnecessary attributes for many search rules, so the ability to remove duplicate attributes can greatly improve the clarity of potential system knowledge and reduce the time complexity of finding rules. At the same time, it enables efficient operation and improved adaptability. Because the structure of the neural network is variable, it has strong self-organization, self-learning, nonlinearity and high fault tolerance, but the ability to express and interpret knowledge is very poor. The network parameters lack physical meaning and learning. Therefore, it has become an inevitable trend to form a fuzzy neural network combining the characteristics of the two. Therefore, exploring the organic combination between rough sets and fuzzy neural networks is undoubtedly of great significance for data mining technology research. This paper proposes a data mining method based on the combination of rough set and fuzzy neural network technology. Using the approximate set to discover the rules of the database rules, the initial structure of the fuzzy neural network is determined, and the training data is used to train the network. Since the fuzzy neural network thus constructed has a good topology of data distribution features from the beginning, the network scale can be greatly reduced and the network training speed can be improved. [ABSTRACT FROM AUTHOR]

The spectrum cluster algorithm is opposite in the other cluster algorithm has the obvious superiority that can distinguish the non-raised distribution the cluster, suits extremely in many actual problems. With this prior model this paper combines the fuzzy set system to propose the new data mining algorithm. At the same time, we build a fuzzy comprehensive evaluation model and analyze the evaluation system of physical education. The result shows that the fuzzy comprehensive evaluation method is improved by using the grey relational grade, which avoids the disadvantages of adopting the principle of maximum membership degree. Therefore, this evaluation method can be used in the evaluation of public sports teaching in colleges and universities. [ABSTRACT FROM AUTHOR]

Bayesian belief networks (BBN) and fuzzy cognitive maps (FCM) are two major causal knowledge frameworks that are frequently used in various domains for cause and effect analysis. However, most researchers use these as separate approaches to analyse the cause(s) and effect(s) of an event. In practice, both methods have their own strengths and weaknesses in both causal modelling and causal analysis. In this paper, a combination of BBN and FCM is used in order to model and analyse network intrusions. First, the BBN is learnt from network intrusion data; following this, an FCM is generated from the BBN, using a migration method. A data-mining approach is suitable for use in the construction of a BBN for network intrusion since this is a data-rich domain, while an FCM is appropriate for the intuitive representation of complex domains. The proposed method of network intrusion analysis using both BBN and FCM consists of several stages, in order to leverage the capabilities of each approach in building the causal model and performing causal analysis. Both the intuitive representation of the causal model in FCM and the wide variety of reasoning methods supported by BBN are exploited in this research to facilitate network intrusion analysis. [ABSTRACT FROM AUTHOR]

In crisp logic every object is similar to itself with degree of reflexivity 1, while the degree of reflexivity in fuzzy logic can be any value in the unit interval, [0, 1]. This behaviour of a fuzzy set is used to enlighten the concept of similarity and inclusion measures. This paper is aimed at discovering the relations between the parameters of the transitive members of a family of cardinality-based fuzzy measure. [ABSTRACT FROM AUTHOR]

System inference, i.e., the building of system structure from system behavior, is widely recognized as a critical challenging issue. In System Theory, structure and behavior are at the extreme sides of the hierarchy that defines knowledge about the system. System inference is known as climbing the hierarchy from less to more knowledge. In addition, it is possible only under justifying conditions. In this paper, a new system inference method is proposed. The proposed method extends the process mining technique to extract knowledge from data and to represent complex systems. The modularity, frequency and timing aspects can be extracted from the data. They are integrated together to construct the Fuzzy Discrete Event System Specification (Fuzzy-DEVS) model. The proposed approach consists of three stages: (1) extraction of event logs from data by using the System Entity Structure method; (2) discovery of a transition system, using process discovery techniques; (3) integration of fuzzy methods to automatically generate a Fuzzy-DEVS model from the transition system. The last stage is implemented as a plugin in the Process Mining Framework (ProM) environment. A case study is presented in which Fuzzy-DEVS model is inferred from real life data, and the SimStudio tool is used for its simulation. [ABSTRACT FROM AUTHOR]

Data mining and machine learning methods have been utilized successfully in the past for identifying and forecasting meaningful patterns from data repositories of diverse application domains. However, the high number of dimensions and instances present in large datasets pose great technical challenges to these existing methods of classification and prediction. The presence of noisy data and missing values makes it even tougher to achieve accurate prediction outcomes. A number of hybrid methodologies constituting dimensionality reduction, feature selection and noise removal methods have been proposed in the literature. However, majority of these techniques force the analysts to compromise on accuracy of classification and prediction results. Therefore, there is a strong need of a methodology that not only scales well with the sheer size and volume of data but also provides near to accurate classification and prediction results by effectively handling the noise in data variables. This paper proposes a fuzzy-based methodology which ranks the dimensions in order of importance and exploits Fuzzy Nearest Neighbor (FNN) approaches for accurate classification and prediction. An experimental evaluation on real world datasets, taken from UCI machine learning repository, shows that the proposed approach outperforms the existing classification and prediction methods by employing only a subset of important features to achieve high prediction accuracy rates at multiple levels of data abstraction. [ABSTRACT FROM AUTHOR]

Recently, the topic of data mining has attracted considerable attention from both academia and industry. Data mining is the process of extracting useful knowledge from large amounts of data. Among the types of knowledge to be mined, classification knowledge is the most widely exploited in engineering applications. A variety of methods exist for inductive learning of "crisp" classification knowledge. This paper presents a new inductive learning algorithm called FuzzyRULES that extracts "fuzzy" classification rules from a database of examples. The use of fuzzy sets and fuzzy logic methods not only provides a powerful, flexible approach to handling vagueness and uncertainty, but also increases the expressive power and comprehensibility of the induced classification knowledge. An example involving the induction of process planning rules is used to illustrate the operation of FuzzyRULES. The algorithm has also been compared against conventional crisp and fuzzy rule induction algorithms on several benchmark data sets. The results obtained have shown that FuzzyRULES induces more compact and more accurate classification rule sets. [ABSTRACT FROM AUTHOR]

There are different nonlinear integrals that could be used as an aggregation tool in information fusion and data mining. The Choquet integral with respect to fuzzy measures is one of them. We present some methods to identify fuzzy measures based on the Choquet integral in this paper. An iterative method introduced by Grabisch is discussed with some counterexamples. Furthermore, after removing some restrictions which are used in Grabisch's model, we introduce an algebraic method and a genetic algorithm to identify fuzzy measures and present some experimental results on both artificial and real-world data sets to show their effectiveness. [ABSTRACT FROM AUTHOR]

Representation of temporal knowledge and analysis of temporal data is becoming a good practice for effective classification and prediction. Various semantic levels on knowledge representation schemes have been measured for temporal data. The existing Fuzzy Cognitive Maps (FCMs) facilitate modeling dynamic systems for knowledge representation and reasoning under uncertainty. However, the FCMs are constructed manually and are constrained by the human experts' validation for assessing its reliability and they are lacking in considering temporal features necessary for reasoning in medical applications. This paper proposes a new temporal mining system known as Fuzzy Temporal Cognitive Map (FTCM), which defines a complete discrete temporal extension and fuzzy inference mechanism of FCM. In FTCM, the temporal dependencies of concepts during a particular time interval are measured. This work aims to reduce the complexities of dynamic modeling of a complex causal system by proposing a four layer fuzzy neural network to construct FTCM from the temporal data. In this proposed model, a fuzzy temporal mutual subsethood operator is used to measure the activation spread in the FTCM for automatic quantification of causalities. This FTCM is designed for a set of temporal clinical records, which can be further used for inferencing and prediction in medical diagnosis by generating a set of fuzzy temporal rules using Allen's temporal relationships and fuzzy temporal rules. [ABSTRACT FROM AUTHOR]

In the context of classification most efforts have been devoted to deriving accurate prediction models from a set of examples whose class is supposed to be known with certainty. However, there are situations where class labels are affected by an intrinsic vagueness, as in ranking customers for marketing campaigns or credit approval. In this paper we propose a new two-phase fuzzy classification method aimed at generating accurate classification rules when labels are uncertain. In the first phase, an ensemble method is applied in order to derive the value of the class membership function for each example in the dataset. In the second phase, an optimal classification model is obtained by solving a fuzzy variant of discrete support vector machines. Computational tests performed on benchmark and real world marketing and credit risk datasets show the effectiveness of the proposed method when it is compared to alternative classification techniques. Furthermore, the tests reveal that the new fuzzy discrete SVM model is a robust regularization method capable of generating stable classification rules, reducing the variance of the error and smoothing out the noise due to outliers. [ABSTRACT FROM AUTHOR]

Effective methods for feature and model structure selection are very important for data-driven modeling, data mining, and system identification tasks. This paper presents a new method for selecting important variables (regressors) in nonlinear (dynamic) models with mixed discrete (categorical, fuzzy) and continuous inputs and outputs. The proposed method applies fuzzy association rule mining. The selection process of the important variables is based on two interesting measures of the mined association rules. [ABSTRACT FROM AUTHOR]

Newsday fuzzy systems are applied in many interdisciplinary fields, in which artificial intelligent tools are utilized to analyze different data in the corresponding fields. Under this background, the 2nd International Conference on Fuzzy Systems and Data Mining (FSDM 2016), organized by Macau University of Science and Technology, is scheduled to be held in Macau, December 11-14, 2016. This special issue contains some selected papers related to fuzzy systems, data mining and their applications, which provides some highlights on fuzzy systems and data mining during the past years in East Asia. [ABSTRACT FROM AUTHOR]

The Fuzzy Logic and system are widely used in many fields during the last decades. Many tools and methods are developed to mine the data in the interdisciplinary science, which witnesses the novel discovery in Science and new technologies in Engineering. To exchange the ideas and research in East Asia, the 2015 International Conference on Fuzzy System and Data Mining (FSDM 2015) is held during 12th~15th December 2015. This special issue is selected some papers related to the fuzzy system and its application in East Asia, which will have a snapshot of the regional research in the field. [ABSTRACT FROM AUTHOR]