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This paper presents a novel combination of filter features selection algorithms for classification problem. Feature selection is one of the most important issues in pattern recognition, machine learning and computer vision. The main objective of feature selection regards the dimensionality reduction, the performance of machine learning improvement and the process comprehensibility increase. Exhaustive...
Feature subset selection is of immense importance in the field of data mining. The increased dimensionality of data makes testing and training of general classification method difficult. This paper presents the development of a model for classifying Pima Indian diabetic database (PIDD). The model consists of two stages. In the first stage, genetic algorithm (GA) and Correlation based feature selection...
In this paper, we propose a novel feature selection method based on spatial coverage relations of features in multidimensional data space. As a filter solution, the algorithm can evaluate the weight of each feature by calculating the spatial coverage relations of features of instances with the same and different class labels in multidimensional data space. And the approach is simple to implement....
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