Exploring ranklets performances in mammographic mass classification using recursive feature elimination

Masotti, Matteo (2005) Exploring ranklets performances in mammographic mass classification using recursive feature elimination. [Preprint]
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Abstract

The ranklet transform is a recent image processing technique characterized by a multi–resolution and orientation selective approach similar to that of the wavelet transform. Yet, differently from the latter, it deals with the ranks of the pixels rather than with their gray–level intensity values. In this paper ranklets are used as classification features for a mammographic mass classification problem. Their performances are explored recursively eliminating some of the less discriminant ranklets coefficients according to the cost function of a Support Vector Machine (SVM) classifier. Experiments show good classification performances even after a significant reduction of the number of ranklet coefficients.

Abstract
Document type
Preprint
Creators
CreatorsAffiliationORCID
Masotti, Matteo
Keywords
Ranklets, Wavelets, Support Vector Machine, Recursive Feature Elimination
Subjects
DOI
Deposit date
02 Mar 2005
Last modified
06 May 2015 07:53
URI

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