TITLE

CLUSTERING BASED POLYHEDRAL CONIC FUNCTIONS ALGORITHM IN CLASSIFICATION

AUTHOR(S)
OZTURK, GURKAN; CIFTCI, MEHMET TAHIR
PUB. DATE
July 2015
SOURCE
Journal of Industrial & Management Optimization;Jul2015, Vol. 11 Issue 3, p921
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
In this study, a new algorithm based on polyhedral conic func tions (PCFs) is developed to solve multi-class supervised data classiffication problems. The k PCFs are constructed for each class in order to separate it from the rest of the data set. The k-means algorithm is applied to find vertices of PCFs and then a linear programming model is solved to calculate the parameters of each PCF. The separating functions for each class are obtained as a pointwise minimum of the PCFs. A class label is assigned to the test point according to its minimum value over all separating functions. In order to demonstrate the performance of the proposed algorithm, it is applied to solve classification problems in publicly available data sets. The comparative results with some mainstream classifiers are presented.
ACCESSION #
99641346

 

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