Characteristic selection method based on sample characteristic distribution confusion degree

Abstract

The present invention discloses a characteristic selection method based on sample characteristic distribution confusion degree, which comprises the following steps: 1) sorting the value sets of each characteristic fi of all kinds of samples in the data set X in an ascending order; 2) determining the range of the limited value of the characteristic fi corresponding to each kind of the sample; 3) determining the number of confusing samples corresponding to the ith characteristic of the M-kind sample in the data set, and calculating the characteristic distribution confusion degree of the ith characteristic in the data set X so as to use the same method to obtain the Confusion value of each characteristic in the data set X; 4) ranking the characteristics in the data set X according to their significance and the obtained Confusion values to obtain an ordered characteristic set F; and 5) based on the set subset search strategy, using a classifier to search ordered characteristic set F or the subset Fsub formed by the partial characteristics in the ordered feature set F to obtain a desired characteristic subset D. The method can select the better-performance characteristic subsets, which improves the recognition capability for the characteristic subsets, and reduces the search times in the subset searching process.

Description

technical field [0001] The invention relates to a dimensionality reduction method, in particular to a feature selection method based on the confusion degree of sample feature value distribution. Background technique [0002] Feature selection is a method of feature dimensionality reduction. Unlike dimensionality reduction methods such as principal component analysis, feature selection is to solve a combinatorial optimization problem in which the amount of computation increases exponentially with the number of features. In the absence of prior assumptions about the relevant knowledge in the specific research field, to select a feature subset containing all important information from the original feature set, we can only exhaustively traverse all possible feature subsets. In this case In this case, as long as the number of features is a little more, the amount of calculation will become very large. Feature selection mainly includes four basic steps: generation of candidate fe...

AI Technical Summary

Problems solved by technology

The filtering feature selection algorithm has high selection efficiency, relatively small amount of calculation, fast speed, and strong versatility, but the performance of the selected feature subset is usually poor

The feature subset selected by the package feature selection algorithm has a good classification effect, and the feature dimension of the selected feature subset is relatively small, but the computational complexity is high, and the speed is slower than the filtering method selection. poor generalization ability

Although the filtering feature selection method has a fast calculation speed and can quickly evaluate the features, the performance of the selected feature subset is generally poor.

The embedded feature selection method embeds the selection method into the learning algorithm, which can only be adapted to certain types of algorithms

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