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Fusion Approaches of Feature Selection Algorithms for Classification Problems

机译：分类问题的特征选择算法融合方法

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摘要

The large amount of data produced by applications in recent years needs to be analyzed in order to extract valuable underlying information from them. Machine learning algorithms are useful tools to perform this task, but usually it is necessary to reduce complexity of data using feature selection algorithms. As usual, many algorithms were proposed to reduce dimension of data, each one with its own advantages and drawbacks. The variety of algorithms leads to either choose one algorithm or to combine several methods. The last option usually brings better performance. Based on this, this paper proposes an analysis of two distinct approaches of combining feature selection algorithms (decision and data fusion). This analysis was made in supervised classification context using real and synthetic datasets. Results showed that one proposed approach (decision fusion) has achieved the best results for the majority of datasets.
机译：为了从应用程序中提取有价值的基础信息，近年来需要分析应用程序生成的大量数据。机器学习算法是执行此任务的有用工具，但通常有必要使用特征选择算法来降低数据的复杂性。像往常一样，提出了许多减少数据量的算法，每种算法都有其自身的优缺点。算法的多样性导致选择一种算法或组合多种方法。最后一个选项通常会带来更好的性能。基于此，本文提出了两种结合特征选择算法（决策和数据融合）的不同方法的分析。该分析是在监督分类的情况下使用实际和综合数据集进行的。结果表明，一种建议的方法（决策融合）已对大多数数据集取得了最佳结果。

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