Feature Selection Methods for Predicting Household Food Insecurity
Dorsewamy1, Mersha Nigus2

1Dorsewamy, Professor, Department of Computer Science, Computer Science at Mangalore University Karnataka, India.
2Mersha Nigus, Master of Science in Information technology, Teachers Education (MSc), Adama Science and Technology University Karnataka, India.

Manuscript received on April 28, 2020. | Revised Manuscript received on May 15, 2020. | Manuscript published on May 30, 2020. | PP: 1560-1568 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2382059120/2020©BEIESP | DOI: 10.35940/ijrte.A2382.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Feature selection is a method of dimension reduction that is used to select a specific subset of appropriate features from the original features by removing unnecessary and redundant features that do not have a benefit in classification or prediction. In this paper, the feature selection approach was conducted using three feature selection methods namely: Filter based, Wrapper based and Embedded based to predict household food insecurity from the household income, consumption, and expenditure survey data (HICE). To implement the above feature selection methods, we proposed new hybrid method by integrating the filter based feature selection methods which is Feature importance, Univariate (chi-square) and Correlation coefficient. To validate the efficiency of the proposed feature selection methods, we used five classification algorithms namely: K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB).
Keywords: Machine learning, Feature selection, Food insecurity, Classification, HICE.
Scope of the Article: Machine learning