A Novel Classifier based on Firefly Algorithm

Abstract

During the past decade, assigning objects to one of several predefined classes with higher accuracy has received considerable focus among researchers. Hence, many algorithms such as statistical algorithms have been developed to solve classification problem. Recently, considerable literature has grown up around swarm intelligence algorithms. Firefly algorithm is a swarm intelligence algorithm that imitates the behavior of the firefly in nature. In this research, a novel classifier approach based on firefly algorithm is introduced as a supervised learning algorithm. Classification based on firefly algorithm is processed by simulating the behavior of firefly in attracting other mates based on intensity and distances. The full process of the proposed algorithm comprises three phases. Feature selection phase, which is responsible for reducing features and picking the most informative features, model construction phase responsible for picking firefly class presenters, and model usage/prediction phase responsible for allocating testing or unseen samples in their relative classes using class presenters. Seven different datasets have been used to test classifier performance. Some of the datasets results were compared with ant-miner algorithm. Results proved that firefly classifier algorithm is a promising and competitive classifier.

Keywords

• Firefly algorithm
• Model construction
• Model prediction
• Swarm intelligence
• Classification