@article{MAKHILLAJIT20212056839,
    title = {Feature Selection for Household Insecurity Classification: Wrapper Approach},
    journal = {Asian Journal of Information Technology},
    volume = {20},
    number = {5},
    pages = {146-151},
    year = {2021},
    issn = {1682-3915},
    doi = {ajit.2021.146.151},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2021.146.151},
    author = {Mersha and},
    keywords = {HICE,classification,feature selection,Food insecurity,ROC},
    abstract = {Feature selection will become crucial,
specifically in facts units with a huge variety of variables
and features. It’s going to cast off irrelevant variables and
boom classification accuracy and overall performance.
With the intention to decrease the model’s computational
cost and growth its efficiency, it is a good concept to
reduce the variety of input variables. This study employs
a wrapper approach to discover a subset of features most
relevant to the classification problem. Sequential
backward series, sequential forward choice and recursive
feature exclusion are the 3 forms of feature selection that
Wrapper procedures help. Machine learning classifiers
inclusive of k-Nearest Neighbor, Logistic Regression,
support vector machine and random forest are used to
determine the classification accuracy of selected
attributes. The findings reveal that the random forest
classifier is the excellent and sequential backward
selection with seven attributes is the great filtering
approach with 99.97% accuracy and a 100% ROC.
Finally, the experiment result of the paper inform to
government, policy makers and humanitarian
organizations to take an emergency action to fix the
problems of household who are food insecure and needs
emergency action to survive their lives.}
    }