Feature selection algorithm based on dynamic programming and K-means clustering

Abstract

The invention discloses a feature selection algorithm based on dynamic programming and K-means clustering. The feature selection algorithm includes that 1) data preprocessing is carried out to mainly solve the problems of data repetition and data attribute value missing in the feature data; 2) feature sub-sets are pre-selected by means of dynamic programming core idea and the within-class and between-class distance is taken as the performance function in the dynamic programming decision process; 3) an original K-means clustering algorithm is improved, the feature sub-sets generated at the dynamic programming stage are clustered by means of an improved K-means algorithm to reject redundant features, and the selected sub-sets are optimized. Based on the feature selection algorithm, the feature sub-sets being low in noise, high in correlation, and free of redundancy can be selected, the effective dimensionality reduction can be realized, the generalization ability and learning efficiency of the machine learning algorithm are improved, the running time of the algorithm is reduced, and finally a simple, efficient and easy-understand learning model is generated.

Description

technical field [0001] The invention relates to the field of feature engineering and machine learning, in particular to a feature selection algorithm based on dynamic programming and K-means clustering. Background technique [0002] The core idea of ​​dynamic programming is: decompose the complex original problem into several simple sub-problems, several sub-problems can also be called several stages, then the solution process of the original problem is transformed into a process of solving multiple stages, through these sub-problems (multi-stage) solution to obtain the solution of the original problem. The objective conditions at the beginning of each stage are called the state of the stage. When the state of a certain stage is determined, different decisions can often be made to enter the next stage. This kind of decision is called decision-making. The judgment basis for making a decision is the corresponding performance function, and the sequence formed by the decisions ...

AI Technical Summary

Problems solved by technology

However, the K-means clustering algorithm itself has the disadvantages that the number of clusters cannot be determined, and it is easy to fall into a local optimal solution. At the same time, when using the K-means clustering algorithm alone for feature selection, it cannot guarantee that the selected feature subset has low noise. , strongly correlated features

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