Rough feature selection for intelligent classifiers

Abstract. The last two decades have seen many powerful classification systems being built for large-scale real-world applications. However, for all their accuracy, one of the persistent obstacles facing these systems is that of data dimensionality. To enable such systems to be effective, a redundancy-removing step is usually required to pre-process the given data. Rough set theory offers a useful, and formal, methodology that can be employed to reduce the dimensionality of datasets. It helps select the most information rich features in a dataset, without transforming the data, all the while attempting to minimise information loss during the selection process. Based on this observation, this paper discusses an approach for semantics-preserving dimensionality reduction, or feature selection, that simplifies domains to aid in developing fuzzy or neural classifiers. Computationally, the approach is highly efficient, relying on simple set operations only. The success of this work is illustrated by applying it to addressing two real-world problems: industrial plant monitoring and medical image analysis.

Rough Set Classifier Based on DSmT

International audienceThe classifier based on rough sets is widely used in pattern recognition. However, in the implementation of rough set-based classifiers, there always exist the problems of uncertainty. Generally, information decision table in Rough Set Theory (RST) always contains many attributes, and the classification performance of each attribute is different. It is necessary to determine which attribute needs to be used according to the specific problem. In RST, such problem is regarded as attribute reduction problems which aims to select proper candidates. Therefore, the uncertainty problem occurs for the classification caused by the choice of attributes. In addition, the voting strategy is usually adopted to determine the category of target concept in the final decision making. However, some classes of targets cannot be determined when multiple categories cannot be easily distinguished (for example, the number of votes of different classes is the same). Thus, the uncertainty occurs for the classification caused by the choice of classes. In this paper, we use the theory of belief functions to solve two above mentioned uncertainties in rough set classification and rough set classifier based on Dezert-Smarandache Theory (DSmT) is proposed. It can be experimentally verified that our proposed approach can deal efficiently with the uncertainty in rough set classifiers

Fast and Accurate 3D Face Recognition Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region classifiers

In this paper we present a new robust approach for 3D face registration to an intrinsic coordinate system of the face. The intrinsic coordinate system is defined by the vertical symmetry plane through the nose, the tip of the nose and the slope of the bridge of the nose. In addition, we propose a 3D face classifier based on the fusion of many dependent region classifiers for overlapping face regions. The region classifiers use PCA-LDA for feature extraction and the likelihood ratio as a matching score. Fusion is realised using straightforward majority voting for the identification scenario. For verification, a voting approach is used as well and the decision is defined by comparing the number of votes to a threshold. Using the proposed registration method combined with a classifier consisting of 60 fused region classifiers we obtain a 99.0% identification rate on the all vs first identification test of the FRGC v2 data. A verification rate of 94.6% at FAR=0.1% was obtained for the all vs all verification test on the FRGC v2 data using fusion of 120 region classifiers. The first is the highest reported performance and the second is in the top-5 of best performing systems on these tests. In addition, our approach is much faster than other methods, taking only 2.5 seconds per image for registration and less than 0.1 ms per comparison. Because we apply feature extraction using PCA and LDA, the resulting template size is also very small: 6 kB for 60 region classifiers