Pareto-based multi-objective optimization for classification in data mining

This paper explores the possibility of classification based on Pareto multi-objective optimization. The efforts on solving optimization problems using the Pareto-based MOO methodology have gained increasing impetus on comparison of selected constraints. Moreover we have different types of classification problem based on optimization model like single objective optimization, MOO, Pareto optimization and convex optimization. All above techniques fail to generate distinguished class/subclass from existing class based on sensitive data. However, in this regard Pareto-based MOO approach is more powerful and effective in addressing various data mining tasks such as clustering, feature selection, classification, and knowledge extraction. The primary contribution of this paper is to solve such noble classification problem. Our work provides an overview of the existing research on MOO and contribution of Pareto based MOO focusing on classification. Particularly, the entire work deals with association of sub-features for noble classification. Moreover potentially interesting sub-features in MOO for classification are used to strengthen the concept of Pareto based MOO. Experiment has been carried out to validate the theory with different real world data sets which are more sensitive in nature. Finally, experimental results provide effectiveness of the proposed method using sensitive data.