Fuzzy Mixed Integer Optimization Model for Regression Approach
This work addresses regression challenges in statistics and data mining by introducing a fuzzy optimization approach, though it appears incremental as it builds on existing mixed integer methods.
The paper tackles regression problems with large-scale, imprecise data by applying a Fuzzy Mixed Integer Optimization Model (FMIOM), which partitions data into polyhedral regions with distinct coefficients, and shows that FMIOM often outperforms leading methods in computational experiments.
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.