Feature Selection by Genetic Algorithm and Information Theory
نویسندگان
چکیده
منابع مشابه
Hybrid Feature Selection Using Genetic Algorithm and Information Theory
In pattern classification, feature selection is an important factor in the performance of classifiers. In particular, when classifying a large number of features or variables, the accuracy and computational time of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. The proposed method consists of two parts: a wrapper part with...
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Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...
متن کاملSequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR
Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...
متن کاملFeature Selection Using Genetic Algorithm with Mutual Information
Feature selection is the problem of selecting a subset of features without reducing the accuracy of representing the original set of features. It is the most important step that affects the performance of a pattern recognition system. In this paper, genetic algorithm (GA) is used to implement a feature selection in filter based method, and the mutual information is served as a fitness function ...
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ژورنال
عنوان ژورنال: Journal of Korean Institute of Intelligent Systems
سال: 2008
ISSN: 1976-9172
DOI: 10.5391/jkiis.2008.18.1.094