Power Distribution Fault Cause Identification With Imbalanced Data Using the Data Mining-Based Fuzzy Classification $E$-Algorithm

Learning is the ability to improve behavior based on former experiences and observations. Nowadays, mankind continuously attempts to train computers for his purpose, and make them smarter through trainings and experiments. Learning machines are a branch of artificial intelligence with the aim of reaching machines able to extract knowledge (learning) from the environment. Classical, fuzzy classification, as a subcategory of machine learning, has an important role in reaching these goals in this area. In the present chapter, we undertake to elaborate and explain some useful and efficient methods of classical versus fuzzy classification. Moreover, we compare them, investigating their advantages and disadvantages.

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Fuzzy Classification in Shipwreck Scatter Analysis

Handbook of Research on Fuzzy Information Processing in Databases ◽

10.4018/978-1-59904-853-6.ch020 ◽

2011 ◽

pp. 516-537 ◽

Cited By ~ 1

Author(s):

Yauheni Veryha ◽

Jean-Yves Blot ◽

Joao Coelho

Keyword(s):

Data Mining ◽

Information Systems ◽

Fuzzy Sets ◽

Relational Databases ◽

Maritime Archaeology ◽

Fuzzy Classification ◽

Fuzzy Sets Theory ◽

Report Generation ◽

Classification Framework ◽

Sets Theory

There are many well-known applications of fuzzy sets theory in various fields of science and technology. However, we think that the area of maritime archaeology did not attract enough attention from researchers of fuzzy sets theory in the last decades. In this chapter, we present examples of problems arising in shipwreck scatter analysis where fuzzy classification may be very useful. Using a real-world example of fragments of ceramics from an ancient shipwreck, we present an exemplary application of the fuzzy classification framework with SQL querying for data mining in archaeological information systems. Our framework can be used as a data mining tool. It can be relatively easily integrated with conventional relational databases, which are widely used in existing archaeological information systems. The main benefits of using our fuzzy classification approach include flexible and precise data analysis with userfriendly information presentation at the report generation phase.

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Application of the Gravitational Search Algorithm for Constructing Fuzzy Classifiers of Imbalanced Data

Symmetry ◽

10.3390/sym11121458 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1458 ◽

Cited By ~ 1

Author(s):

Marina Bardamova ◽

Ilya Hodashinsky ◽

Anton Konev ◽

Alexander Shelupanov

Keyword(s):

Extreme Values ◽

Search Algorithm ◽

Geometric Mean ◽

Gravitational Search Algorithm ◽

Imbalanced Data ◽

Fuzzy Classification ◽

Quality Metric ◽

Fuzzy Classifiers ◽

Minimum Number ◽

Gravitational Search

The presence of imbalance in data significantly complicates the classification task, including fuzzy systems. Due to a large number of instances of bigger classes, instances of smaller classes are not recognized correctly. Therefore, additional tools for improving the quality of classification are required. The most common methods for handling imbalanced data have several disadvantages. For example, methods for generating additional instances of minority classes can worsen classification if there is a strong overlap of instances from different classes. Methods that directly modify the fuzzy classification algorithm lead to a decline in the interpretability of the model. In this paper, we study the efficiency of the gravitational search algorithm in the tasks of selecting the features and tuning the term parameters for fuzzy classifiers of imbalanced data. We consider only data with two classes and apply the algorithm based on extreme values of classes to construct models with a minimum number of rules. In addition, we propose a new quality metric based on the sum of the overall accuracy and the geometric mean with the presence of a priority coefficient between them.

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Fuzzyc-Means Clustering for Data with Clusterwise Tolerance Based onL2- andL1-Regularization

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2011.p0068 ◽

2011 ◽

Vol 15 (1) ◽

pp. 68-75 ◽

Cited By ~ 4

Author(s):

Yukihiro Hamasuna ◽

◽

Yasunori Endo ◽

Sadaaki Miyamoto ◽

Keyword(s):

Data Mining ◽

Objective Function ◽

Clustering Algorithms ◽

Fuzzy Classification ◽

Numerical Examples ◽

Cluster Shape ◽

Regularization Techniques ◽

Single Objective

Detecting various kinds of cluster shape is an important problem in the field of clustering. In general, it is difficult to obtain clusters with different sizes or shapes by single-objective function. From that sense, we have proposed the concept of clusterwise tolerance and constructed clustering algorithms based on it. In the field of data mining, regularization techniques are used in order to derive significant classifiers. In this paper, we propose another concept of clusterwise tolerance from the viewpoint of regularization. Moreover, we construct clustering algorithms for data with clusterwise tolerance based onL2- andL1-regularization. After that, we describe fuzzy classification functions of proposed algorithms. Finally, we show the effectiveness of proposed algorithms through numerical examples.

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Comparative Study in Determining Features Extraction for Islanding Detection using Data Mining Technique: Correlation and Coefficient Analysis

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i3.pp1112-1124 ◽

2017 ◽

Vol 7 (3) ◽

pp. 1112 ◽

Cited By ~ 1

Author(s):

Aziah Khamis ◽

Yan Xu ◽

Azah Mohamed

Keyword(s):

Data Mining ◽

Power Distribution ◽

Distribution System ◽

Harmonic Distortion ◽

Rate Of Change ◽

Support Vector ◽

Islanding Detection ◽

Data Mining Technique ◽

Detection Scheme ◽

Power Distribution System

A comprehensive comparison study on the datamining based approaches for detecting islanding events in a power distribution system with inverter-based distributed generations is presented. The important features for each phase in the island detection scheme are investigated in detail. These features are extracted from the time-varying measurements of voltage, frequency and total harmonic distortion (THD) of current and voltage at the point of common coupling. Numerical studies were conducted on the IEEE 34-bus system considering various scenarios of islanding and non-islanding conditions. The features obtained are then used to train several data mining techniques such as decision tree, support vector machine, neural network, bagging and random forest (RF). The simulation results showed that the important feature parameters can be evaluated based on the correlation between the extracted features. From the results, the four important features that give accurate islanding detection are the fundamental voltage THD, fundamental current THD, rate of change of voltage magnitude and voltage deviation. Comparison studies demonstrated the effectiveness of the RF method in achieving high accuracy for islanding detection.

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