ACO, natural agents applied to feature selection
Speaker
Abstract
| The ecological success of social insects has been attributed to work efficiency achieved by division of labor among the workers of a colony, whereby each worker specializes in a subset of the required tasks. Task allocation and coordination occurs mostly without any central control, especially for large colonies. Instead, individuals respond to simple local cues, for example the pattern of interactions with other individuals, or chemical signals. Also very interesting, is that task allocation is dynamic, based on external and internal factors. Even though certain groups of individuals may specialize in certain tasks, task switching occurs when environmental conditions demand such switches. |
| Nature inspired algorithms like ant colony optimization have been successfully applied to a large number of difficult combinatorial problems like quadratic assignment, traveling salesman problems, routing in telecommunication networks, scheduling, machine learning and feature selection. |
| One of the most important techniques in data preprocessing for data mining is feature selection. Real world data analysis, data mining, classification and modeling problems usually involve a large number of candidate inputs or features. Less relevant or highly correlated features decrease, in general, the classification accuracy, and enlarge the complexity of the classifier. Feature selection is a multi-criteria optimization problem, with contradictory objectives, which are difficult to properly describe by conventional cost functions. Ant colony optimization is particularly attractive for feature selection since no reliable heuristic is available for finding the optimal feature subset, so it is expected that the ants discover good feature combinations as they proceed through the search space. An ant colony optimization algorithm using two cooperative ant colonies for feature selection is presented, which minimizes two objectives: number of features and classification error. Two pheromone matrices and two different heuristics are used for each objective. |
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| Contact information: |
| Dr. Wolf Ketter |
