Abstract
This paper proposed a feature selection strategy based on rough set theory (RST) and discrete particle swarm optimization (DPSO) methods prior to classify protein function. RST is adopted in the first phase with the aim to eliminate the insignificant features and prepared the reduce features to the next phase. In the second phase, the reduced features are optimized using the new evolutionary computation method, DPSO. The optimum features from this two methods were mined using Support Vector Machine Classifier with the optimum RBF's kernel parameters. These methods have greatly reduced the features and achieved higher classification accuracy across the selected datasets compared to full features and RST alone. The results also demonstrate that the integration of RST and DPSO is capable of searching the optimal features for protein classification and applicable to different classification problem.
| Original language | English |
|---|---|
| Title of host publication | 2009 2nd Conference on Data Mining and Optimization, DMO 2009 |
| Pages | 71-78 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2009 |
| Event | 2009 2nd Conference on Data Mining and Optimization, DMO 2009 - Bangi, Selangor Duration: 27 Oct 2009 → 28 Oct 2009 |
Other
| Other | 2009 2nd Conference on Data Mining and Optimization, DMO 2009 |
|---|---|
| City | Bangi, Selangor |
| Period | 27/10/09 → 28/10/09 |
Fingerprint
Keywords
- Data mining
- Feature selection
- Machine learning
- Optimization
- Particle swarm optimization
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Software
Cite this
Filter-wrapper approach to feature selection using RST-DPSO for mining protein function. / Rahman, Shuzlina Abdul; Abu Bakar, Azuraliza; Mohamed Hussein, Zeti Azura.
2009 2nd Conference on Data Mining and Optimization, DMO 2009. 2009. p. 71-78 5341906.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Filter-wrapper approach to feature selection using RST-DPSO for mining protein function
AU - Rahman, Shuzlina Abdul
AU - Abu Bakar, Azuraliza
AU - Mohamed Hussein, Zeti Azura
PY - 2009
Y1 - 2009
N2 - This paper proposed a feature selection strategy based on rough set theory (RST) and discrete particle swarm optimization (DPSO) methods prior to classify protein function. RST is adopted in the first phase with the aim to eliminate the insignificant features and prepared the reduce features to the next phase. In the second phase, the reduced features are optimized using the new evolutionary computation method, DPSO. The optimum features from this two methods were mined using Support Vector Machine Classifier with the optimum RBF's kernel parameters. These methods have greatly reduced the features and achieved higher classification accuracy across the selected datasets compared to full features and RST alone. The results also demonstrate that the integration of RST and DPSO is capable of searching the optimal features for protein classification and applicable to different classification problem.
AB - This paper proposed a feature selection strategy based on rough set theory (RST) and discrete particle swarm optimization (DPSO) methods prior to classify protein function. RST is adopted in the first phase with the aim to eliminate the insignificant features and prepared the reduce features to the next phase. In the second phase, the reduced features are optimized using the new evolutionary computation method, DPSO. The optimum features from this two methods were mined using Support Vector Machine Classifier with the optimum RBF's kernel parameters. These methods have greatly reduced the features and achieved higher classification accuracy across the selected datasets compared to full features and RST alone. The results also demonstrate that the integration of RST and DPSO is capable of searching the optimal features for protein classification and applicable to different classification problem.
KW - Data mining
KW - Feature selection
KW - Machine learning
KW - Optimization
KW - Particle swarm optimization
UR - http://www.scopus.com/inward/record.url?scp=72449194733&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=72449194733&partnerID=8YFLogxK
U2 - 10.1109/DMO.2009.5341906
DO - 10.1109/DMO.2009.5341906
M3 - Conference contribution
SN - 9781424449446
SP - 71
EP - 78
BT - 2009 2nd Conference on Data Mining and Optimization, DMO 2009
ER -