Dr. Mahdi Roozbeh
Dr. Mahdi Roozbeh
Semnan University, Iran
Title: A heuristic algorithm to model the high-dimensional data sets using machine learning
Machine learning is an up-to-date class and modern of strong tools that can combat to many important problems that may be faced with them nowadays. Support vector machine (SVM) is one of these approaches that is really known for classification. Support vector regression (SVR) is a way to build a regression model which is an incredible member of the machine learning family, too. Recently, high-dimensional data sets are the most challenging problem that may be faced. The main problems in high-dimensional data are the estimation of the coefficients and interpretation. In the high-dimension problems, classical techniques same as ordinary least-squares approach which is the best method if the essential assumptions such as normality and independency to the error terms as well as a little or no multicollinearity in the explanatory variables are met, are not applicable because of a large number of predictor variables. SVR is one of the best methods to model the high-dimensional data sets. It is a really reliable and robust approach to have a good fit with high accuracy. In this research, SVR is used in a real high-dimension dataset about the gene expression in eye disease and then compares it with well-known methods: least absolute shrinkage and selection operator (LASSO) and sparse least trimmed squared (SLTS) methods. Based on the numerical result, SVR methods was the best method to model and predict the real world data set using some goodness of fit criteria such as MSE (mean squares error), MAE (mean absolute error) and RMSE (root mean squared error), after that SLTS was better than LASSO method.


Keywords: Cross validation; High-dimensional data set; Ordinary least square method; Outliers; Robust regression.
My name is Dr. Mahdi Roozbeh and I am an Associate Professor of Mathematics, Statistics, and Computer Science Department since 2018 at Semnan University. I graduated with a Ph.D. in Statistics from the Ferdowsi University of Mashhad in 2011, and I am an expert in regression modeling and analysis of high-dimensional data. I am winner of ISI (International Statistical Institute) Jan Tinbergen Award (International Statistical Study Fund Foundation for the young statisticians and the prize for excellence in statistics is awarded every two years) in 2011 (which hold every 2 years in the world) for the best paper award during the World Statistical Congress in Dublin, awards for Research Excellence at Semnan University in 2015; 2017; 2018, Prof Behboodian Award and selected as the second Iranian young researcher in Statistics by Iranian Statistical Institute (2018), Elected for World Bank Trust Fund in the 62nd ISI WSC in Kuala Lumpur, 2019.
My research focuses on high-dimensional deep learning, robust estimation and semiparametric regression modeling, and I am an expert in machine learning and published more than 40 papers indexed by ISI base yet.