Wrapper based Feature Selection using Integrative
Teaching Learning Based Optimization Algorithm
Mohan Allam and Nandhini
Malaiyappan
Department of
Computer Science, Pondicherry University, India
Abstract: The performance of
the machine learning models mainly relies on the key features available in the
training dataset. Feature selection is a significant job for pattern
recognition for finding an important group of features to build classification
models with a minimum number of features. Feature selection with optimization
algorithms will improve the prediction rate of the classification models. But,
tuning the controlling parameters of the optimization algorithms is a
challenging task. In this paper, we present a wrapper-based model called Feature
Selection with Integrative Teaching Learning Based Optimization (FS-ITLBO),
which uses multiple teachers to select the optimal set of features from feature
space. The goal of the proposed algorithm is to search the entire solution
space without struck in the local optima of features. Moreover, the proposed
method only utilizes teacher count parameter along with the size of the population
and a number of iterations. Various classification models have been used for
finding the fitness of instances in the population and to estimate the
effectiveness of the proposed model. The robustness of the proposed algorithm
has been assessed on Wisconsin Diagnostic Breast Cancer (WDBC) as well as
Parkinson’s Disease datasets and compared with different wrapper-based feature
selection techniques, including genetic algorithm and Binary Teaching Learning Based
Optimization (BTLBO). The outcomes have confirmed that FS-ITLBO model produced the
best accuracy with the optimal subset of features.
Keywords: Feature Selection,
Integrative Teaching Learning based Optimization, Genetic Algorithm, Breast
Cancer.
Received May 15, 2019; accepted April 10, 2020