Journal ArticleDOI
An effective attribute clustering approach for feature selection and replacement
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TLDR
An attribute clustering method based on genetic algorithms is proposed for feature selection and feature replacement that combines both the average accuracy of attribute substitution in clusters and the cluster balance as the fitness function.Abstract:
Feature selection is an important preprocessing step in mining and learning. A good set of features cannot only improve the accuracy of classification, but can also reduce the time to derive rules. It is executed especially when the amount of attributes in a given training data is very large. In this article, an attribute clustering method based on genetic algorithms is proposed for feature selection and feature replacement. It combines both the average accuracy of attribute substitution in clusters and the cluster balance as the fitness function. Experimental comparison with the k-means clustering approach and all combinations of attributes also shows the proposed approach can get a good trade-off between accuracy and time complexity. Besides, after feature selection, the rules derived from only the selected features may usually be hard to use if some values of the selected features cannot be obtained in current environments. This problem can be easily solved in our proposed approach. The attributes with...read more
Citations
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Journal ArticleDOI
Feature Selection with Attributes Clustering by Maximal Information Coefficient
TL;DR: This paper proposes a unsupervised feature selection method which could reuse a specific data exploration result and follows the idea of clustering attributes and combines two state-of-the-art data analyzing methods, that's maximal information coefficient and affinity propagation.
Journal ArticleDOI
Using group genetic algorithm to improve performance of attribute clustering
TL;DR: This study improves the performance of the GA-based attribute clustering process based on the grouping genetic algorithm (GGA).
Proceedings ArticleDOI
An evolutionary attribute clustering and selection method based on feature similarity
TL;DR: The previous GA-based clustering method for attribute clustering and feature selection is modified for a better execution performance based on feature similarity and feature dependence.
Journal ArticleDOI
Subspace Selective Ensemble Algorithm Based on Feature Clustering
TL;DR: A feature-clustering-based subspace selective ensemble learning algorithm was proposed to improve ensemble classifier performance, allowing for high dimensional data sets, and the results showed the classification accuracy increase significantly.
Journal Article
Finding minimal reducts from incomplete information systems
TL;DR: A theorem is proved on the basis of defined binary matrix and asymmetry relation, and a genetic algorithm based on this theorem is proposed for finding the minimal reducts from incomplete information systems.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
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Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.