TY  - JOUR
T1  - Performance Evaluation of Classification Models for Household Income, Consumption and
Expenditure Data Set
AU - Nigus, Mersha AU - , Dorsewamy 
JO  - Asian Journal of Information Technology
VL  - 20
IS  - 5
SP  - 134
EP  - 140
PY  - 2021
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2021.134.140
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2021.134.140
KW  - Machine learning
KW  -classification
KW  -HICE
KW  -food insecurity
KW  -KNN
AB  - Food security is more prominent on the policy
agenda today than it has been in the past, thanks to recent
food shortages at both the regional and global levels as
well as renewed promises from major donor countries to
combat chronic hunger. One field where machine learning
can be used is in the classification of household food
insecurity. In this study, we establish a robust
methodology to categorize whether or not a household is
being food secure and food insecure by machine learning
algorithms. In this study, we have used ten machine
learning algorithms to classify the food security status of
the Household. Gradient Boosting (GB), Random Forest
(RF), Extra Tree (ET), Bagging, K-Nearest Neighbor
(KNN), Decision Tree (DT), Support Vector Machine
(SVM), Logistic Regression (LR), Ada Boost (AB) and
Naive Bayes were the classification algorithms used
throughout this study (NB). Then, we perform
classification tasks from developing data set for
household food security status by gathering data from
HICE survey data and validating it by Domain Experts.
The performance of all classifiers has better results for all
performance metrics. The performance of the Random
Forest and Gradient Boosting models are outstanding with
a testing accuracy of 0.9997 and the other classifier such
as Bagging, Decision tree, Ada Boost, Extra tree,
K-nearest neighbor, Logistic Regression, SVM and Naive
Bayes are scored 0.9996, 0.09996, 0.9994, 0.95675,
0.9415, 0.8915, 0.7853 and 0.7595, respectively.
ER  -