Improving the efficiency of a mixed integer linear programming based approach for multi-class classification problem

Authors:
Alaleh Maskooki
Affiliations:
-
Venue:
Computers and Industrial Engineering
Year:
2013

Citing 7
Cited 0

Polyhedral separability through successive LP

Journal of Optimization Theory and Applications
Pattern classification by concurrently determined piecewise linear and convex discriminant functions

Computers and Industrial Engineering
A mixed integer optimisation model for data classification

Computers and Industrial Engineering
A new mathematical programming approach to multi-group classification problems

Computers and Operations Research
Combining a new data classification technique and regression analysis to predict the Cost-To-Serve new customers

Computers and Industrial Engineering
Multisurface method of pattern separation

IEEE Transactions on Information Theory
General mathematical programming formulations for the statistical classification problem

Operations Research Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Data classification is one of the fundamental issues in data mining and machine learning. A great deal of effort has been done for reducing the time required to learn a classification model. In this research, a new model and algorithm is proposed to improve the work of Xu and Papageorgiou (2009). Computational comparisons on real and simulated patterns with different characteristics (including dimension, high overlap or heterogeneity in the attributes) confirm that, the improved method considerably reduces the training time in comparison to the primary model, whereas it generally maintains the accuracy. Particularly, this speed-increase is significant in the case of high overlap. In addition, the rate of increase in training time of the proposed model is much less than that of the primary model, as the set-size or the number of overlapping samples is increased.