Optimal Feature Subset Selection Method Based on Structural Vector Complementary of Classification Ability

Abstract

The present invention proposes a new optimal feature subset selection method based on the complementarity of classification capability structure vectors, aiming at the classification capability evaluation criteria in which single values ​​are used as features or feature subsets in most existing methods. This method defines the feature classification ability structure vector and the complementary features of the classification ability structure vector in binary form, and uses the dichotomy method to calculate the threshold of feature classification and discrimination ability in each subcategory problem, and on this basis, according to the different characteristics of the selected feature subset The principle of structural complementarity maximization and greedy strategy are used to select the optimal feature subset. This method not only fully considers the different evaluations of each feature on the classification ability of different categories, but also follows the principle of maximizing the structural complementarity of the classification ability in the process of feature selection. It not only conforms to the natural law of complementary advantages, but also maximizes the feature classification information, so as to obtain a better feature subset, effectively reduce redundant features, and improve the accuracy of classification prediction.

Description

technical field [0001] The invention belongs to the technical field of machine learning and pattern recognition, and specifically proposes a reasonable and effective feature subset selection method. Background technique [0002] Feature selection is one of the two main approaches to dimensionality reduction. It plays a vital role in machine learning and pattern recognition, and it is also one of the basic issues studied in it, and it is a key data preprocessing step in constructing classifiers. Feature selection is based on some evaluation criteria to select a subset of features that are meaningful for classification on the original feature set to remove irrelevant or redundant features, thereby reducing the dimension of the original space to m dimensions that are much smaller than the original dimension. With the rapid development of the Internet and high-throughput technology, we have entered the era of big data, and the data is huge and complex, which also makes the rese...

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Problems solved by technology

However, some work has shown that some features with small scores should also be selected, and some combinations of features with higher classification ability values ​​do not always lead to good classification results.

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