Use of meta-learning for parameter tuning for classification problems

Abstract

Currently, a major task in Machine Learning (ML) that has been used is the classification of data, a task that involves assigning a default unknown class of several known ones. In ML, the pattern classification is an instance of supervised learning and can be modeled by a range of algorithms such as Artificial Neural Networks (ANN), Support Vector Machines (SMV), Decision Trees (DT), Deep Learning (DL), among others. Moreover, many times the values of parameters used in such models contribute directly to their performance, and optimize the configuration of these parameters can improve the performance of these algorithms. Recently, meta-learning concepts have been used to choose appropriate settings of parameter sets for algorithms in ML. The use of meta-learning in conjunction with optimization techniques has shown promising results. To handle with the automated selection, we propose in this project to investigate the use of meta-learning both in the selection of algorithms otimization as its parameter sets for classifiers. The overall goal is to achieve better performance in terms of accuracy and computational cost of techniques and algorithms used in classification problems.