Optimal feature selection algorithm based on quantum-inspired clone genetic strategy in text categorization

ABSTRACT

Information overload is a serious issue in the modern society. As a powerful method to help people out of being "lost" in too much useless information, automatic text categorization is getting more and more important. Feature selection is the most important step in text categorization. To improve the performance of text categorization, we present a new text categorization method called quantum-inspired clone genetic algorithm (QCGA). The experimental results show that the QCGA algorithm is superior to other common methods.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.7 DOCUMENT AND TEXT PROCESSING
      I.7.0 General

Additional Classification:
  E. Data
  E.2 DATA STORAGE REPRESENTATIONS
      Subjects: Contiguous representations**

General Terms:
Algorithms, Documentation

Keywords:
feature selection, genetic algorithm, quantum-inspired clone genetic algorithm, text categorization