Human Activity Recognition for Multi-label Classification in Smart Homes Using Ensemble Methods

Kasubi, John W.; Huchaiah, Manjaiah D.

doi:10.1007/978-3-030-82322-1_21

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…In this research, the first experiment was performed for the individual classifiers and the second was voting, wherein the results of each individual classifier were summed up to form the outcome class using common votes. Kasubi & Huchaiah (2021) have concentrated on distinguishing human physical actions from day-to-day routines using the ARAS dataset. Adaboost collective and bagging techniques of classification were used for experimentation.…”

Section: Related Workmentioning

confidence: 99%

Performance evaluation of machine learning based voting classifier system for human activity recognition

Jindal

Sachdeva

Kushwaha

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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In the last few decades, Human Activity Recognition (HAR) has been a centre of attraction in many research domains, and it is referred to as the potential of interpreting human body gestures through sensors and ascertaining the activity of a human being. The present work has proposed the voting classifier system for human activity recognition. For the voting classifier system, five machine learning classifiers are considered: Logistic Regression (LR), K-Nearest Neighbour (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM). These machine learning classifiers are ensembled by analyzing the best performers among them. The ensemble voting classifiers are proposed under two variations, i.e., hard voting and soft voting. The various combinations of voting classifiers are compared and evaluated. For experiments, the benchmark dataset of the UCI-HAR dataset is considered, and all the data files are combined into a single file to avoid bias. The dimensionality of the dataset is reduced by using Principal Component Analysis (PCA) from 561 features to 200 components. The results reveal that Voting Classifier-II (a combination of SVM, KNN, and LR) using soft voting outperformed other machine learning classifiers.

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Section: Related Workmentioning

confidence: 99%