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| Feature Selection Method Based on Fractal Dimension and Ant Colony Optimization Algorithm |
| NI Li-Ping, NI Zhi-Wei, WU Hao, YE Hong-Yun |
| School of Management, Hefei University of Technology, Hefei 230009 |
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Abstract Feature selection plays an important role in machine learning and data mining as a primary preprocessing step. A feature selection algorithm is presented based on fractal dimension and ant colony optimization algorithm. In this algorithm, fractal dimension is used as an evaluation mechanism and ant colony optimization algorithm is employed to accelerate the selection process. To evaluate the efficiency of the proposed algorithm, the SVM algorithm and K-fold cross validation are utilized to evaluate the classification accuracy on four datasets. The experimental results show the proposed algorithm can identify the better feature space with a great decrease of dataset dimension in a short time.
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Received: 22 February 2008
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2009, Vol. 22 
