Review of Feature Selection Algorithms for Breast Cancer Ultrasound Image

被引:7
|
作者
Verma, Kesari [1 ]
Singh, Bikesh Kumar [2 ]
Tripathi, Priyanka [3 ]
Thoke, A. S. [4 ]
机构
[1] Natl Inst Technol, Dept Comp Applicat, Raipur, Madhya Pradesh, India
[2] Natl Inst Technol Raipur, Dept Biomed Engn, Raipur, Madhya Pradesh, India
[3] Natl Inst Technol Raipur, Dept Comp Applicat, Raipur, Madhya Pradesh, India
[4] Natl Inst Technol Raipur, Dept Elect Engn, Raipur, Madhya Pradesh, India
来源
NEW TRENDS IN INTELLIGENT INFORMATION AND DATABASE SYSTEMS | 2015年 / 598卷
关键词
Feature Selection; Random Forest; Ranking of features; important feature selection;
D O I
10.1007/978-3-319-16211-9_3
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Correct classification of patterns from images is one of the challenging tasks and has become the focus of much research in areas of machine learning and computer vision in recent era. Images are described by many variables like shape, texture, color and spectral for practical model building. Hundreds or thousands of features are extracted from images, with each one containing only a small amount of information. The selection of optimal and relevant features is very important for correct classification and identification of benign and malignant tumors in breast cancer dataset. In this paper we analyzed different feature selection algorithms like best first search, chi-square test, gain ratio, information gain, recursive feature elimination and random forest for our dataset. We also proposed a ranking technique to all the selected features based on the score given by different feature selection algorithms.
引用
收藏
页码:23 / 32
页数:10
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