Fuzzy Mixed Integer Optimization Model for Regression Approach
  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.
