Texture descriptors for representing feature vectors.


Pattern representation affects classification performance. Although discovering "universal" features that work for many classification problems is ideal, most representations are problem specific. In this paper, we improve the classification performance of a classifier system by transforming a one-dimensional input descriptor into a two-dimensional space so that effective texture extractors can be extracted to capture hidden data information. We develop two new methods for matrix representation where features are extracted that are more generalizable. The first method for generating a two-dimensional representation of patterns is based on trees, the objective being to reshape the feature vector into a matrix, and the second method performs mathematical operations to build a matrix representation. The proposed framework is then evaluated for its specific power on three medical problems. To evaluate generalizability, we compare the proposed approaches with several other baseline methods across some well-known benchmark datasets that reflect a diversity of classification problems. Not only does our approach show high robustness, but it also exhibits low sensitivity to parameters. When different approaches for transforming a vector into a matrix are combined with several texture descriptors, the resulting system often works well without requiring any ad-hoc optimization. The performance of the tested systems is compared by Wilcoxon signed rank test and Friedman's test.

Keywords Protein representations, PSSM, AAS, matrix representations

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