Hybrid model of Correlation based Filter Feature Selection and Machine Learning classifiers applied on Smart Meter Data set

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dc.contributor.authorSinayobye, Janvier Omar
dc.contributor.authorKaawaase Kyanda, Swaib
dc.contributor.authorKiwanuka, N. Fred
dc.contributor.authorMusabe, Richard
dc.date.accessioned2023-01-25T07:45:07Z
dc.date.available2023-01-25T07:45:07Z
dc.date.issued2019
dc.description.abstractFeature 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.en_US
dc.identifier.citationSinayobye, 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.2019.00009en_US
dc.identifier.other10.1109/SEiA.2019.00009
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/7194
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectFeature selectionen_US
dc.subjectFeature Extractionen_US
dc.subjectSmart meter data setsen_US
dc.subjectMachine Learningen_US
dc.titleHybrid model of Correlation based Filter Feature Selection and Machine Learning classifiers applied on Smart Meter Data seten_US
dc.typeArticleen_US
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