Imbalanced Datasets Classification by Fuzzy Rule Extraction and Genetic Algorithms

Soler, V.; Cerquides, Jesús; Sabrià, J.; Roig, Jordi; Prim, Marta

doi:10.1109/icdmw.2006.95

Cited by 14 publications

(7 citation statements)

References 7 publications

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“…For using a GFS with an imbalanced dataset, the standard fitness function has to be altered, or else changes must be effected on the dataset for raising the importance of misclassifying instances of the minority class. Both techniques have been well studied in the context of GFSs: there are works that deal with the use of costs in fuzzy classifiers for imbalanced datasets 14,44,47,48,52 , and other studies suggest employing a preprocessing step in order to balance the training data before the training 3,19,20,21,22 . In this last respect, we can highlight the re-sampling procedure named "Synthetic Minority Oversampling Technique" or SMOTE 7 ; it has been shown that SMOTE is one the most efficient preprocessing algorithms for imbalanced data in relation with Fuzzy Rule Based Systems (FRBS) 19 .…”

Section: Introductionmentioning

confidence: 99%

Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers

Sánchez¹,

Couso²

2012

IJCIS

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Determining whether an imprecise dataset is imbalanced is not immediate. The vagueness in the data causes that the prior probabilities of the classes are not precisely known, and therefore the degree of imbalance can also be uncertain. In this paper we propose suitable extensions of different resampling algorithms that can be applied to interval valued, multi-labelled data. By means of these extended preprocessing algorithms, certain classification systems designed for minimizing the fraction of misclassifications are able to produce knowledge bases that are also adequate under common metrics for imbalanced classification.

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Section: Introductionmentioning

confidence: 99%

Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers

Sánchez¹,

Couso²

2012

IJCIS

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“…One of the approaches that follows a design with specific operations for the imbalance is the one described in [94], renamed in [95] as FLAGID, Fuzzy Logic And Genetic algorithms for Imbalanced Datasets. This approach follow several stages, starting with a first step that is a modified version of the RecBF/DDA algorithm [96].…”

Section: Efs and Algorithm-level Approachesmentioning

confidence: 99%

Evolutionary Fuzzy Systems: A Case Study in Imbalanced Classification

Fernández

Herrera

2016

Fuzzy Logic and Information Fusion

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The use of evolutionary algorithms for designing fuzzy systems provides them with learning and adaptation capabilities, resulting on what is known as Evolutionary Fuzzy Systems. These types of systems have been successfully applied in several areas of Data Mining, including standard classification, regression problems and frequent pattern mining. This is due to their ability to adapt their working procedure independently of the context we are addressing. Specifically, Evolutionary Fuzzy Systems have been lately applied to a new classification problem showing good and accurate results. We are referring to the problem of classification with imbalanced datasets, which is basically defined by an uneven distribution between the instances of the classes. In this work, we will first introduce some basic concepts on linguistic fuzzy rule based systems. Then, we will present a complete taxonomy for Evolutionary Fuzzy Systems. Then, we will review several significant proposals made in this research area that have been developed for addressing classification with imbalanced datasets. Finally, we will show a case study from which we will highlight the good behavior of Evolutionary Fuzzy Systems in this particular context.

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“…For using a GFS with an imbalanced dataset, either we can alter the fitness function by including a cost matrix [27] or we can preprocess the data. Both techniques have already been studied in the context of GFSs: there are works that deal with the use of fuzzy classifiers for the imbalanced dataset problem [10], [34], [36], [37], [41], and others that employ a preprocessing step in order to balance the training data before the training, which has been shown to solve the problem [1], [12], [13], [14], [15]. In particular, there is an study in [12] about the combination of imbalanced classes in the framework of FRBS and the application of a re-sampling procedure named "Synthetic Minority Oversampling Technique" or SMOTE [2].…”

Section: Introductionmentioning

confidence: 99%

Preprocessing vague imbalanced datasets and its use in genetic fuzzy classifiers

Sánchez

Couso

2010

International Conference on Fuzzy Systems

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When there is a substantial difference between the number of cases of the majority and minority classes, minimum error-based classification systems tend to overlook these last instances. This can be corrected either by preprocessing the dataset or by altering the objective function of the classifier. In this paper we analyze the first approach, in the context of genetic fuzzy systems (GFS), and in particular of those that can operate with imprecisely observed and low quality data. We will analyze the different preprocessing mechanisms of imbalanced datasets and will show the necessity of extending these for solving those problems where the data is both imprecise and imbalanced. In addition, we include a comprehensive description of a new algorithm, able to preprocess imprecise imbalanced datasets. Several real-world datasets are used to evaluate the proposal.

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Imbalanced Datasets Classification by Fuzzy Rule Extraction and Genetic Algorithms

Cited by 14 publications

References 7 publications

Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers

Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers

Evolutionary Fuzzy Systems: A Case Study in Imbalanced Classification

Preprocessing vague imbalanced datasets and its use in genetic fuzzy classifiers

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