Article,
Feature Selection using Stepwise ANOVA Discriminant Analysis for Mammogram Mass Classification
International Journal on Signal & Image Processing, 2 (1): 4 (January 2011)
Abstract
In this paper, a feature selection method using
stepwise Analysis Of Variance (ANOVA) Discriminant
Analysis (DA) is used for classifying mammogram masses.
This approach combines the 17 shape and margin properties
of the mass regions and classifies the masses as benign or
malignant using ANOVA DA. ANOVA DA provides wilk’s
lambda statistics for each feature and its level of significance.
In ANOVA DA the discriminating power of each feature is
estimated based on grouping class variable. Principal
component analysis (PCA) does feature extraction but it
doesn’t consider the grouping class variable. The experiment
is performed on 300 DDSM database mammogram images.
The stepwise ANOVA DA and PCA dimension reduction
methods are used to reduce the number of features used. The
feature selection using stepwise ANOVA DA is better as it
analyses the data according to grouping class variable.
Stepwise ANOVA DA based feature selection gives reduced
feature set, with high classification accuracy.
