European Journal of Computer Science and Information Technology (EJCSIT)

EA Journals

Comparative Analysis of Selected Machine Learning Algorithms Based On Generated Smart Home Dataset

Abstract

There has being recent interest in applying machine learning techniques in smart homes for the purpose of securing the home. This paper presents the comparative study on six classification algorithms based on generated smart home datasets. These includes Logistics Regression, Support vector machine, Random forest, K-Nearest Neighbor, Decision Tree and Gaussian Naïve Bayes. Two different smart home datasets were generated and used to train and test the algorithms. The confusion matrix was used to evaluate the outputs of the classifiers. From the confusion matrix, Prediction Accuracy, Precision, Recall and F1-Score of the models were calculated. The Support Vector Machine (SVM) outperformed the other algorithms in terms of accuracy on both datasets with values of 67.89 and 88.56 respectively. The SVM and Logistics Regression also maintained the highest precision of 100.0 as compared to the other algorithms.

 

Keywords: classification algorithms, smart home, support vector machine

cc logo

This work by European American Journals is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License

 

Recent Publications

Email ID: editor.ejcsit@ea-journals.org
Impact Factor: 7.80
Print ISSN: 2054-0957
Online ISSN: 2054-0965
DOI: https://doi.org/10.37745/ejcsit.2013

Author Guidelines
Submit Papers
Review Status

 

Scroll to Top

Don't miss any Call For Paper update from EA Journals

Fill up the form below and get notified everytime we call for new submissions for our journals.