Abstract
Feature selection is a key issue in the advanced application fields like data mining, multi-dimensional statistical analysis, multimedia index and document classification. It is a novel method to exploit fractal dimension to reduce dimension of feature spaces. The most famous one is the fractal dimension based feature selection algorithm FDR proposed by Traina Jr et al. This paper proposes an optimized algorithm, OptFDR, which scans the dataset only once and avoids the efficiency problems of multiple scanning large dataset in the algorithm FDR. The performance experiments are made for evaluating OptFDRalgorithm using real-world image feature dataset and synthetic dataset with fractal characteristics. The experimental results show that OptFDR algorithm outperforms FDR algorithm.
Supported by the National Natural Science Foundation of China under Grant No.60173051, the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of the Ministry of Education, China.
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Bao, Y., Yu, G., Sun, H., Wang, D. (2004). Performance Optimization of Fractal Dimension Based Feature Selection Algorithm. In: Li, Q., Wang, G., Feng, L. (eds) Advances in Web-Age Information Management. WAIM 2004. Lecture Notes in Computer Science, vol 3129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27772-9_82
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DOI: https://doi.org/10.1007/978-3-540-27772-9_82
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