Proceedings ArticleDOI
A survey of genetic feature selection in mining issues
Maria J. Martin-Bautista,Maria-Amparo Vila +1 more
- Vol. 2, pp 1314-1321
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TLDR
A survey of the approaches presented in the literature to select relevant features by using genetic algorithms is given and the different values of the genetic parameters utilized as well as the fitness functions are compared.Abstract:
In this paper, we review the feature selection problem in mining issues. The application of soft computing techniques to data mining and knowledge discovery is now emerging in order to enhance the effectiveness of the traditional classification methods coming from machine learning. A survey of the approaches presented in the literature to select relevant features by using genetic algorithms is given. The different values of the genetic parameters utilized as well as the fitness functions are compared. A more detailed review of the proposals in the mining fields of databases, text and the Web is also given.read more
Citations
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Journal ArticleDOI
Hybrid genetic algorithms for feature selection
TL;DR: Experiments revealed that the proposed hybrid GA is superior to both a simple GA and sequential search algorithms, and showed better convergence properties compared to the classical GAs.
Special Issue on data mining and knowledge discovery with evolutionary algorithms
Ashish Ghosh,Alex A. Freitas +1 more
TL;DR: This book integrates two areas of computer science, namely data mining and evolutionary algorithms, and emphasizes the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making.
Journal ArticleDOI
Data mining in soft computing framework: a survey
TL;DR: A survey of the available literature on data mining using soft computing based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model is provided.
Book ChapterDOI
A survey of evolutionary algorithms for data mining and knowledge discovery
TL;DR: This chapter discusses the use of evolutionary algorithms, particularly genetic algorithms and genetic programming, in data mining and knowledge discovery, and discusses some preprocessing and postprocessing steps of the knowledge discovery process, focusing on attribute selection and pruning of an ensemble of classifiers.
Journal ArticleDOI
Web mining in soft computing framework: relevance, state of the art and future directions
TL;DR: The paper summarizes the different characteristics of Web data, the basic components of Web mining and its different types, and the current state of the art.
References
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Book
An Introduction to Genetic Algorithms
TL;DR: An Introduction to Genetic Algorithms focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
Proceedings Article
A Comparative Study on Feature Selection in Text Categorization
Yiming Yang,Jan O. Pedersen +1 more
TL;DR: This paper finds strong correlations between the DF IG and CHI values of a term and suggests that DF thresholding the simplest method with the lowest cost in computation can be reliably used instead of IG or CHI when the computation of these measures are too expensive.
Journal ArticleDOI
Feature Selection for Classification
Manoranjan Dash,Huan Liu +1 more
TL;DR: This survey identifies the future research areas in feature selection, introduces newcomers to this field, and paves the way for practitioners who search for suitable methods for solving domain-specific real-world applications.
Book ChapterDOI
Irrelevant features and the subset selection problem
TL;DR: A method for feature subset selection using cross-validation that is applicable to any induction algorithm is described, and experiments conducted with ID3 and C4.5 on artificial and real datasets are discussed.
Book
Knowledge Discovery in Databases
Gregory Piateski,William Frawley +1 more
TL;DR: Knowledge Discovery in Databases brings together current research on the exciting problem of discovering useful and interesting knowledge in databases, which spans many different approaches to discovery, including inductive learning, bayesian statistics, semantic query optimization, knowledge acquisition for expert systems, information theory, and fuzzy 1 sets.