Helmholtz principle based supervised and unsupervised feature selection methods for text mining


Tutkan M., GANİZ M. C., AKYOKUŞ S.

INFORMATION PROCESSING & MANAGEMENT, cilt.52, sa.5, ss.885-910, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 52 Sayı: 5
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1016/j.ipm.2016.03.007
  • Dergi Adı: INFORMATION PROCESSING & MANAGEMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Sayfa Sayıları: ss.885-910
  • Anahtar Kelimeler: Feature selection, Attribute selection, Machine learning, Text mining, Text classification, Helmholtz principle, SEMANTIC SMOOTHING METHOD, ALGORITHM
  • Marmara Üniversitesi Adresli: Evet

Özet

One of the important problems in text classification is the high dimensionality of the feature space. Feature selection methods are used to reduce the dimensionality of the feature space by selecting the most valuable features for classification. Apart from reducing the dimensionality, feature selection methods have potential to improve text classifiers' performance both in terms of accuracy and time. Furthermore, it helps to build simpler and as a result more comprehensible models. In this study we propose new methods for feature selection from textual data, called Meaning Based Feature Selection (MBFS) which is based on the Helmholtz principle from the Gestalt theory of human perception which is used in image processing. The proposed approaches are extensively evaluated by their effect on the classification performance of two well-known classifiers on several datasets and compared with several feature selection algorithms commonly used in text mining. Our results demonstrate the value of the MBFS methods in terms of classification accuracy and execution time. (C) 2016 Elsevier Ltd. All rights reserved.