Redefining selection of features and classification algorithms for room occupancy detection

Nor Samsiah Sani, Illa Iza Suhana Shamsuddin, Shahnorbanun Sahran, Abdul Hadi Abd Rahman , Ereena Nadjimin Muzaffar

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The exponential growth of todays technologies has resulted in the growth of high-throughput data with respect to both dimensionality and sample size. Therefore, efficient and effective supervision of these data becomes increasing challenging and machine learning techniques were developed with regards to knowledge discovery and recognizing patterns from these data. This paper presents machine learning tool for preprocessing tasks and a comparative study of different classification techniques in which a machine learning tasks have been employed in an experimental set up using a data set archived from the UCI Machine Learning Repository website. The objective of this paper is to analyse the impact of refined feature selection on different classification algorithms to improve the prediction of classification accuracy for room occupancy. Subsets of the original features constructed by filter or information gain and wrapper techniques are compared in terms of the classification performance achieved with selected machine learning algorithms. Three feature selection algorithms are tested, specifically the Information Gain Attribute Evaluation (IGAE), Correlation Attribute Evaluation (CAE) and Wrapper Subset Evaluation (WSE) algorithms. Following a refined feature selection stage, three machine learning algorithms are then compared, consisting the Multi-Layer Perceptron (MLP), Logistic Model Trees (LMT) and Instance Based k (IBk). Based on the feature analysis, the WSE was found to be optimal in identifying relevant features. The application of feature selection is certainly intended to obtain a higher accuracy performance. The experimental results also demonstrate the effectiveness of Instance Based k compared to other ML classifiers in providing the highest performance rate of room occupancy prediction.

Original languageEnglish
Pages (from-to)1486-1493
Number of pages8
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume8
Issue number4-2
Publication statusPublished - 1 Jan 2018

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artificial intelligence
Learning systems
Feature extraction
Set theory
Learning algorithms
Neural Networks (Computer)
prediction
Multilayer neural networks
logit analysis
Growth
Sample Size
Data mining
Machine Learning
Logistics
Websites
Classifiers
Logistic Models
methodology
Throughput
Technology

Keywords

  • Algorithms
  • Classifications
  • Feature selection
  • IBk
  • LMT
  • Machine learning
  • MLP

ASJC Scopus subject areas

  • Computer Science(all)
  • Agricultural and Biological Sciences(all)
  • Engineering(all)

Cite this

Redefining selection of features and classification algorithms for room occupancy detection. / Sani, Nor Samsiah; Shamsuddin, Illa Iza Suhana; Sahran, Shahnorbanun; Abd Rahman , Abdul Hadi; Muzaffar, Ereena Nadjimin.

In: International Journal on Advanced Science, Engineering and Information Technology, Vol. 8, No. 4-2, 01.01.2018, p. 1486-1493.

Research output: Contribution to journalArticle

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