Abstract
Feature selection, the process of selecting a feature subset from the original feature set, plays an important role in a wide variety of contexts such as data mining, machine learning, and pattern recognition. Recently, fractal dimension has been exploited to reduce the dimensionality of the data space. FDR(Fractal Dimensionality Reduction) is one of the most famous fractal dimension based feature selection algorithm proposed by Traina in 2000. However, it is inefficient in the high dimensional data space for multiple scanning the dataset. Take advantage of the Z-ordering technique, this paper proposed an optimized FDR, ZBFDR(Z-ordering Based FDR), which can select the feature subset through scanning the dataset once except for preprocessing. The experimental results show that ZBFDR algorithm achieves better performance.
This work is sponsored by the National Natural Science Foundation of China (No.60573096), and the Opening Foundation of the Key Laboratory of Opto-Electronic Technology and Intelligent Control (Lanzhou Jiaotong University), Ministry of Education,China,Grant No. K04116.
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Yan, G., Li, Z., Yuan, L. (2006). The Practical Method of Fractal Dimensionality Reduction Based on Z-Ordering Technique. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_60
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DOI: https://doi.org/10.1007/11811305_60
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