Affiliations: Machine Intelligence Unit, Indian Statistical Institute,
Kolkata - 700 108, India. {sriparna_r,sanghami}@isical.ac.in
Note: [] Address for correspondence: Machine Intelligence Unit, Indian
Statistical Institute, Kolkata - 700 108, India
Abstract: In this paper, a novel point symmetry based pattern classifier (PSC)
is proposed. A recently developed point symmetry based distance is utilized to
determine the amount of point symmetry of a particular test pattern with
respect to a class prototype. Kd-tree based nearest neighbor search is used for
reducing the complexity of point symmetry distance computation. The proposed
point symmetry based classifier is well-suited for classifying data sets having
point symmetric classes, irrespective of any convexity, overlap or size. In
order to classify data sets having line symmetry property, a line symmetry
based classifier (LSC) along the lines of PSC is thereafter proposed in this
paper. To measure the total amount of line symmetry of a particular point in a
class, a new definition of line symmetry based distance is also provided.
Proposed LSC preserves the advantages of PSC. The performance of PSC and LSC
are demonstrated in classifying fourteen artificial and real-life data sets of
varying complexities. For the purpose of comparison, k-NN classifier and the
well-known support vector machine (SVM) based classifiers are executed on the
data sets used here for the experiments. Statistical analysis, ANOVA, is also
performed to compare the performance of these classification techniques.
Keywords: Pattern Classification, Point Symmetry, Kd-tree, Symmetry based distance, Nearest Neighbor Rule, Line Symmetry
