Aggregation Pheromone Density Based Pattern Classification

• Autorzy: Halder A. , Ghosh A. , Ghosh S.
• Języki publikacji: EN

Abstrakty

EN

The study of ant colonies behavior and their self-organizing capabilities is of interest to machine learning community, because it provides models of distributed adaptive organization which are useful to solve difficult optimization and classification problems among others. Social insects like ants, bees deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone, that causes clumping behavior in a species and brings individuals into a closer proximity, is called aggregation pheromone. This article presents a new algorithm (called, APC) for pattern classification based on this property of aggregation pheromone found in natural behavior of real ants. Here each data pattern is considered as an ant, and the training patterns (ants) form several groups or colonies depending on the number of classes present in the data set. A new test pattern (ant) will move along the direction where average aggregation pheromone density (at the location of the new ant) formed due to each colony of ants is higher and hence eventually it will join that colony. Thus each individual test pattern (ant) will finally join a particular colony. The proposed algorithm is evaluated with a number of benchmark data sets as well as various kinds of artificially generated data sets using three evaluationmeasures. Results are compared with four other well known conventional classification techniques. Experimental results show the potentiality of the proposed algorithm in terms of all the evaluation measures compared to other algorithms.

Słowa kluczowe

EN

swarm intelligence; ant colony optimization; Aggregation pheromone; pattern classification

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2009
• Tom: Vol. 92, nr 4
• Strony: 345--362
• Opis fizyczny: Bibliogr. 38 poz., tab., wykr.

Twórcy

autor

Halder A.

autor

Ghosh A.

autor

Ghosh S.

• Center for Soft Computing Research, Indian Statistical Institute, Kolkata 700108, India,

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