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Redefining Selection of Features and Classification Algorithms for Room Occupancy Detection

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@article{IJASEIT6826,
   author = {Nor Samsiah Sani and Illa Iza Suhana Shamsuddin and Shahnurbanon Sahran and Abdul Hadi Abd Rahman and Ereena Nadjimin Muzaffar},
   title = {Redefining Selection of Features and Classification Algorithms for Room Occupancy Detection},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {8},
   number = {4-2},
   year = {2018},
   pages = {1486--1493},
   keywords = {Feature selection, machine learning, classifications, algorithms, MLP, LMT, IBk.},
   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 dataset 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.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6826},    doi = {10.18517/ijaseit.8.4-2.6826} }

EndNote

%A Sani, Nor Samsiah
%A Shamsuddin, Illa Iza Suhana
%A Sahran, Shahnurbanon
%A Abd Rahman, Abdul Hadi
%A Muzaffar, Ereena Nadjimin
%D 2018
%T Redefining Selection of Features and Classification Algorithms for Room Occupancy Detection
%B 2018
%9 Feature selection, machine learning, classifications, algorithms, MLP, LMT, IBk.
%! Redefining Selection of Features and Classification Algorithms for Room Occupancy Detection
%K Feature selection, machine learning, classifications, algorithms, MLP, LMT, IBk.
%X 

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 dataset 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.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6826 %R doi:10.18517/ijaseit.8.4-2.6826 %J International Journal on Advanced Science, Engineering and Information Technology %V 8 %N 4-2 %@ 2088-5334

IEEE

Nor Samsiah Sani,Illa Iza Suhana Shamsuddin,Shahnurbanon Sahran,Abdul Hadi Abd Rahman and Ereena Nadjimin Muzaffar,"Redefining Selection of Features and Classification Algorithms for Room Occupancy Detection," International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 4-2, pp. 1486-1493, 2018. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.8.4-2.6826.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Sani, Nor Samsiah
AU  - Shamsuddin, Illa Iza Suhana
AU  - Sahran, Shahnurbanon
AU  - Abd Rahman, Abdul Hadi
AU  - Muzaffar, Ereena Nadjimin
PY  - 2018
TI  - Redefining Selection of Features and Classification Algorithms for Room Occupancy Detection
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 8 (2018) No. 4-2
Y2  - 2018
SP  - 1486
EP  - 1493
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Feature selection, machine learning, classifications, algorithms, MLP, LMT, IBk.
N2  - 

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 dataset 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.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6826 DO - 10.18517/ijaseit.8.4-2.6826

RefWorks

RT Journal Article
ID 6826
A1 Sani, Nor Samsiah
A1 Shamsuddin, Illa Iza Suhana
A1 Sahran, Shahnurbanon
A1 Abd Rahman, Abdul Hadi
A1 Muzaffar, Ereena Nadjimin
T1 Redefining Selection of Features and Classification Algorithms for Room Occupancy Detection
JF International Journal on Advanced Science, Engineering and Information Technology
VO 8
IS 4-2
YR 2018
SP 1486
OP 1493
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Feature selection, machine learning, classifications, algorithms, MLP, LMT, IBk.
AB 

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 dataset 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.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6826 DO - 10.18517/ijaseit.8.4-2.6826