H.6.3.2. Feature evaluation and selection
1. H-BwoaSvm: A Hybrid Model for Classification and Feature Selection of Mammography Screening Behavior Data

E. Enayati; Z. Hassani; M. Moodi

Volume 8, Issue 2 , Spring 2020, , Pages 237-245

http://dx.doi.org/10.22044/jadm.2020.8105.1945

Abstract
  Breast cancer is one of the most common cancer in the world. Early detection of cancers cause significantly reduce in morbidity rate and treatment costs. Mammography is a known effective diagnosis method of breast cancer. A way for mammography screening behavior identification is women's awareness evaluation ...  Read More

H.6.3.2. Feature evaluation and selection
2. A Novel Architecture for Detecting Phishing Webpages using Cost-based Feature Selection

A. Zangooei; V. Derhami; F. Jamshidi

Volume 7, Issue 4 , Autumn 2019, , Pages 607-616

http://dx.doi.org/10.22044/jadm.2019.7183.1852

Abstract
  Phishing is one of the luring techniques used to exploit personal information. A phishing webpage detection system (PWDS) extracts features to determine whether it is a phishing webpage or not. Selecting appropriate features improves the performance of PWDS. Performance criteria are detection accuracy ...  Read More

H.6.3.2. Feature evaluation and selection
3. MLIFT: Enhancing Multi-label Classifier with Ensemble Feature Selection

Sh kashef; H. Nezamabadi-pour

Volume 7, Issue 3 , Summer 2019, , Pages 355-365

http://dx.doi.org/10.22044/jadm.2018.5780.1688

Abstract
  Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label ...  Read More

H.6.3.2. Feature evaluation and selection
4. Feature extraction of hyperspectral images using boundary semi-labeled samples and hybrid criterion

M. Imani; H. Ghassemian

Volume 5, Issue 1 , Winter 2017, , Pages 39-53

http://dx.doi.org/10.22044/jadm.2017.787

Abstract
  Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it ...  Read More

H.6.3.2. Feature evaluation and selection
5. Feature extraction in opinion mining through Persian reviews

E. Golpar-Rabooki; S. Zarghamifar; J. Rezaeenour

Volume 3, Issue 2 , Summer 2015, , Pages 169-179

http://dx.doi.org/10.5829/idosi.JAIDM.2015.03.02.06

Abstract
  Opinion mining deals with an analysis of user reviews for extracting their opinions, sentiments and demands in a specific area, which can play an important role in making major decisions in such area. In general, opinion mining extracts user reviews at three levels of document, sentence and feature. ...  Read More

H.6.3.2. Feature evaluation and selection
6. Feature reduction of hyperspectral images: Discriminant analysis and the first principal component

Maryam Imani; Hassan Ghassemian

Volume 3, Issue 1 , Winter 2015, , Pages 1-9

http://dx.doi.org/10.5829/idosi.JAIDM.2015.03.01.01

Abstract
  When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatter ...  Read More