UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 6 Issue 6
June-2019
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIRDB06011


Registration ID:
221434

Page Number

69-74

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Title

A New Machine Learning Model based on Induction of Rules for Autism Detection

Abstract

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.

Key Words

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

Cite This Article

"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

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"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

Publication Details

Published Paper ID: JETIRDB06011
Registration ID: 221434
Published In: Volume 6 | Issue 6 | Year June-2019
DOI (Digital Object Identifier):
Page No: 69-74
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162


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