Hybrid model of Correlation based Filter Feature Selection and Machine Learning classifiers applied on Smart Meter Data set
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Feature selection is referred to the process of
obtaining a subset from an original feature set according to
certain feature selection criterion, which selects the relevant
features of the dataset. It plays a role in compressing the data
processing scale, where the redundant and irrelevant features
are removed. Feature selection techniques show that more
information is not always good in machine learning
applications. Apply different algorithms for the data at hand
and with baseline classification performance values we can
select a final feature selection algorithm. In this paper, we
propose a hybrid classification model, which has correlation
based filter feature selection algorithm and Machine learning
as classifiers. The objective of this study is to select relevant
features and analyze the outperform machine learning
algorithms in order to train our model, predict and compare
their classification performance. In this method, features are
ordered according to their Absolute correlation value with
respect to the class attribute. Then top K Features are selected
from ordered list of features to form a reduced dataset. This
proposed classifier model is applied to our smart meter
datasets. To measure the performance of these selected
features; seven benchmark classifier are used; Random Forest
(RF), Logistic Regression (LR), k-Nearest Neighbor (kNN),
Naïve Bayes (NB), Decision Tree (DT), Linear Discriminant
Analysis (LDA) and Support Vector Machine (SVM). This
paper then analyzes the performance of all classifiers with
feature selection in term of accuracy, sensitivity, F-Measure,
Specificity, Precision, and MCC. From our experiment, we
found that Random Forest classifier performed higher than
other used classifiers.
Description
Keywords
Feature selection, Feature Extraction, Smart meter data sets, Machine Learning
Citation
Sinayobye, J. O., Kaawaase, K. S., Kiwanuka, F. N., & Musabe, R. (2019, May). Hybrid model of correlation based filter feature selection and machine learning classifiers applied on smart meter data set. In 2019 IEEE/ACM Symposium on Software Engineering in Africa (SEiA) (pp. 1-10). IEEE. DOI 10.1109/SEiA.2019.00009