Information Sciences—Intelligent Systems: An International Journal
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Swarm intelligence
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Classification Rule Discovery with Ant Colony Optimization
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Ant Colony Optimization
Aggregation Pheromone Density Based Clustering
ICIT '06 Proceedings of the 9th International Conference on Information Technology
Aggregation pheromone density based data clustering
Information Sciences: an International Journal
Use of aggregation pheromone density for image segmentation
Pattern Recognition Letters
Aggregation pheromone density based image segmentation
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning pattern classification-a survey
IEEE Transactions on Information Theory
Ant based semi-supervised classification
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Understanding critical factors in appearance-based gender categorization
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
The multi-engine ASP solver ME-ASP
JELIA'12 Proceedings of the 13th European conference on Logics in Artificial Intelligence
Aggregation pheromone metaphor for semi-supervised classification
Pattern Recognition
Semi-supervised change detection using modified self-organizing feature map neural network
Applied Soft Computing
Change detection in remotely sensed images using semi-supervised clustering algorithms
International Journal of Knowledge Engineering and Soft Data Paradigms
Information Sciences: an International Journal
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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.