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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

Autism spectrum disorder is a syndrome related to interaction with people and repetitive behavior. ASD is diagnosed by health experts with the help of special practices that can be prolonged and costly. Researchers developed several ASD detection techniques by utilizing machine learning tools. ML provides the advanced algorithms that build automatic classification models. But disease prediction is a challenge for ML models due to the majority of the medical datasets including irrelevant features. Feature selection is a critical job in the predictive modeling for selecting a subset of significant features from the dataset. Recent feature selection techniques are using the optimization algorithms to improve the prediction rate of classification models. Most of the optimization algorithms make use of several controlling parameters that have to be tuned for improved productivity. In this chapter, a novel feature selection technique is proposed using binary teaching learning-based optimization algorithm that requires standard controlling parameters to acquire optimum features from ASD data.