Optimization of attribute selection model using bio-inspired algorithms
Attribute selection which is also known as feature selection is an essential process that is relevant to predictive analysis.To date, various feature selection algorithms have been introduced, nevertheless they all work independently. Hence, reducing the consistency of the accuracy rate. The aim of...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universiti Utara Malaysia
2019
|
| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/25567/1/JICT%2018%201%202019%2035-55.pdf |
| _version_ | 1803628959137529856 |
|---|---|
| author | Basir, Mohammad Aizat Yusof, Yuhanis Hussin, Mohamed Saifullah |
| author_facet | Basir, Mohammad Aizat Yusof, Yuhanis Hussin, Mohamed Saifullah |
| author_sort | Basir, Mohammad Aizat |
| collection | UUM |
| description | Attribute selection which is also known as feature selection is an essential process that is relevant to predictive analysis.To date, various feature selection algorithms have been introduced,
nevertheless they all work independently. Hence, reducing the consistency of the accuracy rate. The aim of this paper is to investigate the use of bio-inspired search algorithms in producing optimal attribute set. This is achieved in two stages; 1) create attribute selection models by combining search method and feature selection algorithms, and 2) determine an optimized attribute set by employing bio-inspired algorithms.Classification performance of the produced attribute set is analyzed based on
accuracy and number of selected attributes. Experimental results conducted on six (6) public real datasets reveal that the feature
selection model with the implementation of bio-inspired search algorithm consistently performs good classification (i.e higher accuracy with fewer numbers of attributes) on the selected data
set. Such a finding indicates that bio-inspired algorithms can contribute in identifying the few most important features to be used in data mining model construction. |
| first_indexed | 2024-07-04T06:30:14Z |
| format | Article |
| id | uum-25567 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:30:14Z |
| publishDate | 2019 |
| publisher | Universiti Utara Malaysia |
| record_format | dspace |
| spelling | uum-255672019-02-12T07:45:04Z https://repo.uum.edu.my/id/eprint/25567/ Optimization of attribute selection model using bio-inspired algorithms Basir, Mohammad Aizat Yusof, Yuhanis Hussin, Mohamed Saifullah QA75 Electronic computers. Computer science Attribute selection which is also known as feature selection is an essential process that is relevant to predictive analysis.To date, various feature selection algorithms have been introduced, nevertheless they all work independently. Hence, reducing the consistency of the accuracy rate. The aim of this paper is to investigate the use of bio-inspired search algorithms in producing optimal attribute set. This is achieved in two stages; 1) create attribute selection models by combining search method and feature selection algorithms, and 2) determine an optimized attribute set by employing bio-inspired algorithms.Classification performance of the produced attribute set is analyzed based on accuracy and number of selected attributes. Experimental results conducted on six (6) public real datasets reveal that the feature selection model with the implementation of bio-inspired search algorithm consistently performs good classification (i.e higher accuracy with fewer numbers of attributes) on the selected data set. Such a finding indicates that bio-inspired algorithms can contribute in identifying the few most important features to be used in data mining model construction. Universiti Utara Malaysia 2019 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/25567/1/JICT%2018%201%202019%2035-55.pdf Basir, Mohammad Aizat and Yusof, Yuhanis and Hussin, Mohamed Saifullah (2019) Optimization of attribute selection model using bio-inspired algorithms. Journal of ICT, 18 (1). pp. 35-55. ISSN 1675-414X http://jict.uum.edu.my/index.php/currentissues#aa3 |
| spellingShingle | QA75 Electronic computers. Computer science Basir, Mohammad Aizat Yusof, Yuhanis Hussin, Mohamed Saifullah Optimization of attribute selection model using bio-inspired algorithms |
| title | Optimization of attribute selection model using bio-inspired algorithms |
| title_full | Optimization of attribute selection model using bio-inspired algorithms |
| title_fullStr | Optimization of attribute selection model using bio-inspired algorithms |
| title_full_unstemmed | Optimization of attribute selection model using bio-inspired algorithms |
| title_short | Optimization of attribute selection model using bio-inspired algorithms |
| title_sort | optimization of attribute selection model using bio inspired algorithms |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/25567/1/JICT%2018%201%202019%2035-55.pdf |
| work_keys_str_mv | AT basirmohammadaizat optimizationofattributeselectionmodelusingbioinspiredalgorithms AT yusofyuhanis optimizationofattributeselectionmodelusingbioinspiredalgorithms AT hussinmohamedsaifullah optimizationofattributeselectionmodelusingbioinspiredalgorithms |