Research on Improvement of Feature Weights in Text Classification

doi:10.3969/j.issn.1006-2475.2018.02.014

Abstract

Abstract: In order to overcome the shortcomings of traditional TF-IDF (Term Frequency Inverse Document Frequency) algorithm, the improved TF-IDF-dist algorithm is proposed by using the distribution of feature words. The experimental results show that the improved algorithm has an average increase of F1 value by 3.2% in the different feature dimensions. With the different feature selection algorithm, the F1 value is increased by 2.75% and the improved TF-IDF-dist algorithm has more adaptability on the imbalance datasets. It shows the validity of the algorithm in text classification.

Key words: machine learning, text classification, feature weights, TF-IDF

CLC Number:

TP306.1

LI Peng-peng， FAN Hui-min. Research on Improvement of Feature Weights in Text Classification[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2018.02.014.

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