Open AccessA Novel Approach of Ensemble Learning with Feature Reduction for Classification of Binary and Multiclass IoT Data
Author(s) -
Vijay Khadse
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i6.4811
Subject(s) - computer science , isomap , artificial intelligence , principal component analysis , linear discriminant analysis , machine learning , data mining , binary number , ensemble learning , dimensionality reduction , data set , feature (linguistics) , internet of things , set (abstract data type) , ensemble forecasting , class (philosophy) , domain (mathematical analysis) , reduction (mathematics) , discriminant , pattern recognition (psychology) , mathematics , nonlinear dimensionality reduction , mathematical analysis , linguistics , philosophy , geometry , arithmetic , programming language , embedded system
The number of network and sensor-enabled devices in the Internet of Things (IoT) domains is growing extremely, leading to a huge production of data. These data contain important information which can be used in various areas, such as science, industry, medical, and even social life. To make the IoT system smart, the only solution is entering the world of machine learning. Many machine learning algorithms are introduced for handling such a huge amount of IoT data. It is very difficult to find the best-suited algorithm for problems in the IoT domain. This study combined three ensemble models and proposed a new model termed the “hybrid model”. A set of features are extracted from the raw IoT datasets from diverse IoT domains, using Principal component analysis (PCA), Linear discriminant analysis (LDA), and Isomap for classification problems. Performance comparison of the classifiers is provided in terms of their accuracy, area under the curve (AUC), and F1 score. This comparative study’s experimental result shows that Hybrid with PCA and Stacking ensemble technique in particular with PCA have better overall performance than other ensemble techniques for binary class and multie class datasets respectively