Feature selection for multiclass classification and regression techniques

Abstract

Online Multiclass Classification (OMC) performs the heterogeneous domain from complex data of completely diverse feature representation. OMC algorithm investigates the problem of heterogeneous domain and regression problems. Most existing studies of online learning divide the online feature selection into two parts. i) Learning with full input and ii) Learning with partial input. To address this limitation, we investigate the heterogeneous and regression problem in which an online learner is allowed to maintain a classifier with limited number of features. The key challenge of OMC algorithm is how to maintain the multiclass classification and regression using the active features. We attempt to tackle this challenge by studying on line feature selection and truncation techniques. We present OMC, Novel algorithm to solve the problems and give their performance analysis. We evaluate the performance of the proposed algorithms for on line learner using different domain and demonstrate their applications in real world problems including image classifications and analysis of bio informatics. Encouraging results validate the efficiency of our techniques.

Technology Intervention: Rethinking the Role of Education and Faculty in the Transformative Digital Environment

“…Predictive analytics has been used for decades in the business world but only recently has been adopted by institutions of higher education. Predictive analytics is the process of analyzing and interpreting meaningful patterns from large amounts of data (Patil, 2015). Daniel (2014) addressed the need for higher education to begin to use Big Data to address the complex problems that they are facing for the future.…”