SEMI SUPERVISED LEARNING WITH MULTI CLASS SIMPLE RECYCLED SELECTION USING SEQUENTIAL FORWARD SELECTION
In semi supervised learning, one frequent problem is how to find useful unlabeled data to join labeled data in building classification models. Simple Recycled Selection (SRS) is a method to rank unlabeled data based on confidence levels to build an ensemble of weak classifiers, working with two class datasets. Sequential Forward Selection (SFS) is a greedy deterministic search strategy usually applied to feature selection, that make use of a wrapper method to inquire the best feature subset to train a model. The objective of the present work is to modify the SFS search strategy to help SRS ensembles in the classification of multi class datasets using the one versus all approach. SRS ensembles of support vector machines, decision trees and nearest neighbors classifiers are trained and tested using the proposed algorithm. Regular versions of support vector machines with SFS, decision trees and nearest neighbors classifiers are used as comparison. Benchmark datasets are used as training and test for the algorithms. Results show that the error rates of SRS ensembles are lower for most two class and multi class datasets. Keywords Semi supervised learning, SRS, SFS, Multi class, Cascade classifier.