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Many applications such as pattern recognition require selecting a subset of the input features in order to represent the whole set of features. The aim of feature selection is to remove irrelevant or redundant features while keeping the most informative ones. In this paper, an ant colony system approach for solving feature selection for classification is presented. The proposed algorithm was tested...
We give sub linear-time approximation algorithms for some optimization problems arising in machine learning, such as training linear classifiers and finding minimum enclosing balls. Our algorithms can be extended to some kernelized versions of these problems, such as SVDD, hard margin SVM, and L2-SVM, for which sub linear-time algorithms were not known before. These new algorithms use a combination...
The paper addresses the feature selection based on Neighborhood Rough Set (NRS) used as evaluation function and Ant Colony Optimization (ACO) as generation procedure. A NRS-based measure is employed as heuristic information of ACO. For the weakness of setting a specified value to the size of neighborhood, a new standard deviation based value is advanced to be the size of neighborhood. Four datasets...
This paper proposes a hybrid feature selection algorithm based on dynamic weighted ant colony algorithm. Features are treated as graph nodes to construct graph model. Ant colony algorithm is used to select features while support vector machine classifier is applied to evaluate the performance of feature subsets, and then feature pheromone is computed and updated based on the evaluation results. At...
Research on the technology of classification the rules of current data mining. Because of accuracy and validity of the classification rules meet, this paper brings up a new algorithm for classification rules: classification rule based on backtracking ant colony algorithm. The core of the algorithm is: the project ant crawl forward, at the same time the backtracking ants crawl backward. If the backtracking...
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