• Mrs. R. Indra
• A. Christy Gilpa

The number of high-dimensional data that endures and is publically accessible on the internet has very developed in the past few years. Therefore, machine learning techniques have the challenge in dealing with the significant number of input features, which is modeling an attractive issue for researchers. To utilize machine learning techniques efficiently, preprocessing of the data is essential. Feature selection is one of the most frequent and prominent methods in data preprocessing, and has become a necessary component of the machine learning process is also known as variable selection, attribute selection, or variable subset selection in machine learning and statistics. It is the method of removing irrelevant and detecting relevant features, noisy data or redundant. This technique speeds up data mining algorithms, enhances comprehensibility and predictive accuracy. Unrelated features are those that give no useful information, and irrelevant features provide no more information than the currently selected features. Regarding supervised inductive learning, feature selection presents a set of candidate features using one of the three approaches. In this research work, a few feature selection techniques like Information Gain, Gain Ratio, Chi-Square and ReliefF are used to reduce the redundant features from the various dataset. These feature selection techniques are implemented by using MATLAB tool. Based on the performance metrics like Accuracy, Kappa Statistics and Error rates, the best feature selection technique to be selected.

Autism Spectrum Disorder, MATLAB, Feature Selection, Classification, Machine Learning, Information Gain, Gain Ratio, Chi-Square and ReliefF.

"A New Machine Learning Model based on Induction of Rules for Autism Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.69-74, June 2019, Available :http://www.jetir.org/papers/JETIRDB06011.pdf

"A New Machine Learning Model based on Induction of Rules for Autism Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp69-74, June 2019, Available at : http://www.jetir.org/papers/JETIRDB06011.pdf