Fusion of Feature Selection with Symbolic Approach for Dimensionality Reduction
نویسنده
چکیده
In this paper, a fusion of two methods for dimensionality reduction is proposed. First method is the selection of features using FQ measure method followed by another method based on symbolic approach is proposed. The symbolic method is based on the transformation of features into symbolic data using the property of collinearity and variance based cumulative sum of features. In this proposed approach, the entire feature set is reduced to only 4 features namely number of line segments, average slope of the line segments, the first and last feature values. Experimentation is performed on the standard datasets WDBC, WBC and WINE and the classification performance is better than the existing methods.
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