Feature Selection Based on Term Frequency Reordering of Document Level | Zendy

Hongfang Zhou | Zendy; Yingjie Zhang | Zendy; Hongjiang Liu | Zendy; Yao Zhang | Zendy

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Feature Selection Based on Term Frequency Reordering of Document Level

Author(s) -

Hongfang Zhou,

Yingjie Zhang,

Hongjiang Liu,

Yao Zhang

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2868844

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

In this paper, we propose a new feature selection algorithm based on term frequency reordering of document level. In our proposed algorithm, it uses the document frequency to weigh the unbalanced factors of the data sets and considers the effect of the term frequency on the feature importance ordering. In the experiments, our proposed algorithm is compared with Normalized Difference Measure, Chi-squared, Odds Ratio, Gini Index, and Balanced Accuracy on the WAP, K1a, K1b RE0, RE1, 20 Newsgroups, Reuters-21578, and RCV1-v2 data sets. The experimental results show that our proposed algorithm is superior to other five algorithms.

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