HDT-HS: A hybrid decision tree/harmony search algorithm for biological datasets

Author

Jaber, K.M. ; Abdullah, Rosni ; Rashid, Nur Aini Abdul

Author_Institution

Dept. of Software Eng., Al-zaytoonah Univ. of Jordan, Amman, Jordan

Volume

1

fYear

2012

fDate

12-14 June 2012

Firstpage

341

Lastpage

345

Abstract

This paper introduces the Hybrid Decision Tree with Harmony Search (HDT-HS) optimization algorithm to improve the rate of accuracy for the decision tree algorithm so as to apply it to DNA data sets. The hybridization includes operating the decision tree method after the Improvisation step of the harmony search algorithm in order to navigate for several solutions at the same time. This is to improve the accuracy of the final results for the decision tree. The results show that the hybrid algorithm achieved better accuracy of about 96.73% compared to classifier algorithms such as Nave (94.8%), MBBC (95.99%); optimization algorithms such as bagging (94.5%) and boosting (94.7%); hybrid decision tree with genetic algorithm (70.7%) and another version from the decision tree such as C4.5 (94.3%) and PCL (94.4%).

Keywords

DNA; bioinformatics; data analysis; decision trees; optimisation; search problems; DNA data sets; HDT-HS optimization algorithm; MBBC; Nave; bagging; biological datasets; boosting; decision tree algorithm; genetic algorithm; harmony search algorithm; hybrid decision tree; hybridization; improvisation step; optimization algorithms; Bagging; Boosting; Decision trees; Genetic algorithms; Genetics; Ice;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer & Information Science (ICCIS), 2012 International Conference on

Conference_Location

Kuala Lumpeu

Print_ISBN

978-1-4673-1937-9

Type

conf

DOI

10.1109/ICCISci.2012.6297266

Filename

6297266