Hybrid Model of Correlation Based Filter Feature Selection and Machine Learning Classifiers Applied on Smart Meter Data Set

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.