Fuzzy Mixed Integer Optimization Model for Regression Approach

TL;DR

This paper introduces a Fuzzy Mixed Integer Optimization Model (FMIOM) for regression that handles data vagueness and large-scale problems by partitioning data into regions with distinct regression coefficients, showing promising results.

Contribution

The paper presents a novel FMIOM approach that combines fuzzy logic with mixed integer optimization for regression, enabling efficient handling of large, imprecise datasets.

Findings

FMIOM is comparable to current leading methods.

FMIOM often outperforms existing approaches.

Method effectively manages large-scale, fuzzy data.

Abstract

Mixed Integer Optimization has been a topic of active research in past decades. It has been used to solve Statistical problems of classification and regression involving massive data. However, there is an inherent degree of vagueness present in huge real life data. This impreciseness is handled by Fuzzy Sets. In this Paper, Fuzzy Mixed Integer Optimization Method (FMIOM) is used to find solution to Regression problem. The methodology exploits discrete character of problem. In this way large scale problems are solved within practical limits. The data points are separated into different polyhedral regions and each region has its own distinct regression coefficients. In this attempt, an attention is drawn to Statistics and Data Mining community that Integer Optimization can be significantly used to revisit different Statistical problems. Computational experimentations with generated and real data sets show that FMIOM is comparable to and often outperforms current leading methods. The results illustrate potential for significant impact of Fuzzy Integer Optimization methods on Computational Statistics and Data Mining.