2013
DOI: 10.1002/widm.1087
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Revisiting evolutionary algorithms in feature selection and nonfuzzy/fuzzy rule based classification

Abstract: This paper discusses the relevance and possible applications of evolutionary algorithms, particularly genetic algorithms, in the domain of knowledge discovery in databases. Knowledge discovery in databases is a process of discovering knowledge along with its validity, novelty, and potentiality. Various genetic-based feature selection algorithms with their pros and cons are discussed in this article. Rule (a kind of high-level representation of knowledge) discovery from databases, posed as single and multiobjec… Show more

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Cited by 10 publications
(8 citation statements)
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References 164 publications
(135 reference statements)
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“…There are three main general problems that involve hybrid algorithms involving SVMs: obtaining SVM (or SVR) best set of parameters (BSPs), feature selection of input variables (FSPs), and kernel selection and combination problems (KSPs) …”
Section: Constructing Novel Algorithms Based On Support Vector Machinesmentioning
confidence: 99%
“…There are three main general problems that involve hybrid algorithms involving SVMs: obtaining SVM (or SVR) best set of parameters (BSPs), feature selection of input variables (FSPs), and kernel selection and combination problems (KSPs) …”
Section: Constructing Novel Algorithms Based On Support Vector Machinesmentioning
confidence: 99%
“…The embedded sensors, IoTs, ubiquitous devices like scanners, bar code readers, and smartphones generate a huge amount of data at an exponential rate, which contributes to the expansion of data size and volume [1] [2] [3]. Intuitively, the valuable hidden knowledge and information in this huge amount of accumulated data could be the potential source to enhance the decision-making capability of the decision-makers of an organization or society [4] [5] [6]. Some of the classification techniques like decision tree (DT), support vector machine (SVM), and random forest [7] [8] have been proven to be effective models for extracting knowledge, that is valid, potential, novel, and finally useful.…”
Section: Introductionmentioning
confidence: 99%
“…In filter approach the feature selection is performed without considering the classification algorithm that will be applied to the selected attributes. [2] Here a subset of attributes that preserves the possible relevant information found in the entire set of attributes is selected. [3] In wrapper approach feature selection is performed by taking into account the classification algorithm that will be applied to the selected attributes.…”
Section: Introductionmentioning
confidence: 99%