A Robust Approach for Mammographic Image Classification Using NSVC Algorithm: Extended Abstract

Nowadays, classification is the most efficient method for breast cancer detection using mammography images. During the last two decades, researchers achieved very good results using different kinds of classification methods. In this context, we will focus in this work on the quality of mammography image classification by proposing a new approach that we compares with the state-of-art methods. Our methods consists of two phases; a semi-supervised step based on SKDA and a second step based on SVM. As will be shown in the experiments section, results on Mammography data set show that the proposed algorithm can get very good results. The best precision obtained by NSVC exceeded 99 %.

[1]  Francesco Camastra,et al.  A Novel Kernel Method for Clustering , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Abbas Bahroudi,et al.  Support vector machine for multi-classification of mineral prospectivity areas , 2012, Comput. Geosci..

[3]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[4]  Jianhua Guo,et al.  Feature subset selection using naive Bayes for text classification , 2015, Pattern Recognit. Lett..

[5]  Jason Weston,et al.  Vicinal Risk Minimization , 2000, NIPS.

[6]  Renguang Zuo,et al.  Support vector machine: A tool for mapping mineral prospectivity , 2011, Comput. Geosci..

[7]  Sang Won Yoon,et al.  Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms , 2014, Expert Syst. Appl..

[8]  Ying Cao,et al.  Advance and Prospects of AdaBoost Algorithm , 2013, ACTA AUTOMATICA SINICA.

[9]  L. Tabár,et al.  The impact of organized mammography service screening on breast carcinoma mortality in seven Swedish counties , 2002, Cancer.

[10]  Ludmil Mikhailov,et al.  An interpretable fuzzy rule-based classification methodology for medical diagnosis , 2009, Artif. Intell. Medicine.

[11]  Quan Pan,et al.  A new belief-based K-nearest neighbor classification method , 2013, Pattern Recognit..

[12]  Jacek M. Leski,et al.  Fuzzy c-varieties/elliptotypes clustering in reproducing kernel Hilbert space , 2004, Fuzzy Sets Syst..

[13]  H. P. Ambulgekar,et al.  Approach of Neural Network to Diagnose Breast Cancer on three different Data Set , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

[14]  Peter I. Rockett,et al.  The use of vicinal-risk minimization for training decision trees , 2015, Appl. Soft Comput..

[15]  V. Rodriguez-Galiano,et al.  Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines , 2015 .

[16]  Xia Li,et al.  Cellular automata for simulating land use changes based on support vector machines , 2008, Comput. Geosci..

[17]  K. Rose.  Deterministic annealing for clustering, compression, classification, regression, and related optimization problems , 1998, Proc. IEEE.

[18]  Mohammad Darzi,et al.  Feature Selection for Breast Cancer Diagnosis: A Case-Based Wrapper Approach , 2011 .

[19]  J. Łȩski.  Fuzzy c-varieties/elliptotypes clustering in reproducing kernel Hilbert space , 2004 .

[20]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[21]  Ruihu Wang,et al.  AdaBoost for Feature Selection, Classification and Its Relation with SVM, A Review , 2012 .

[22]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[23]  Qing Song,et al.  Vicinal support vector classifier using supervised kernel-based clustering , 2014, Artif. Intell. Medicine.

[24]  Sheng Liu,et al.  Mammographic mass detection by vicinal support vector machine , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[25]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .