Affiliations: [a] Artificial Intelligence, CSE and IT Department, Shiraz University, Shiraz, Iran | [b] CSE and IT Department, Shiraz University, Shiraz, Iran
Correspondence:
[*]
Corresponding author: Ali Hamzeh, CSE and IT Department, Shiraz University, Shiraz, Iran. Tel./Fax: +98 7116133165; E-mail:[email protected]
Abstract: Recently, the hybrid Particle Swarm Optimisation/Ant
Colony Optimisation (PSO/ACO) has been proposed for discovery of
classification rules. An improved version of this hybrid scheme, PSO/ACO2
algorithm, can directly cope with nominal attributes without converting them
into numerical ones. Although PSO/ACO2 can handle nominal values, it suffers
from high computational complexity for large datasets. Beside variety of
classification methods which exist to provide more compact set of rules,
this study propose an approach which reduces the computational complexity of
PSO/ACO2 in order to make it suitable for classification of large datasets.
This work is developed the K-mode as a method of sampling from the datasets.
In this regard a modification is employed to this algorithm in order to
decrease the computational time as well as maintaining the accuracy of the
algorithm. Further contribution of this paper is utilizing a new fitness
measure for the algorithm. This measure has a robust theoretical background.
The combination of the proposed modified K-mode method and the introduced
fitness measure led to speed the obtained results up. The experimental
result shows the efficiency of the proposed algorithm in comparison with its
competitors.
