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Classification is one of the fundamental supervised learning tasks which learns classifiers from the given training data and related labels. The quality of labels is important in classification tasks. However, in many real-world scenarios, data annotation is often corrupted, especially when the annotation process is done by humans. Vaguely labeled data is one of the common problems caused by limited domain knowledge or partial data observation. In this paper, a novel method is proposed to classify vaguely labeled data based on evidential fusion. Vaguely labeled data are divided into several small data groups by the proposed valid label-set cover assignment algorithm. Evidence theory is applied to vaguely labeled data classification by regarding each base classifier on a small data group as one piece of evidence. This gives the chance of classifying unseen precise labeled data from related vague labels. Note that our approach is not restricted to any specific classifiers. It can be generalized to any off-the-shelf classification methods with probabilistic outputs. Finally, experiments are conducted on both synthetic data and real-world data with different base classifiers. Experimental results show that the proposed method achieves superior performance against compared methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an approach to aircraft target recognition using Inverse Synthetic Aperture Radar (ISAR) images. The goal of this work is to develop a robust algorithm to add Automated Target Recognition (ATR) capabilities to extract efficient feature vectors and ensure a comprehensive recognition process. In the first part on this paper, we used a database of ISAR images reconstructed from anechoic chamber simulations in order to extract efficient feature vectors. In the second part of this work, we proposed a recognition architecture to perform recognition tasks and provide a human operator with useful information for target recognition tasks. Several kinds of descriptor can be used to acquire information about target characteristics from radar signals and images. Indeed, a number of methods are currently used in automatic target recognition; notably based on ISAR imaging. However, target characteristic extraction from radar echoes remains a difficult task, and the methods are generally specific to either aircraft or ship recognition. In this work, we describe our approach to designing faster, more effective retrieval systems and a comprehensive architecture. Our approach uses global feature vectors for both ship and aircraft recognition. Firstly, the global feature vectors are described by two types of descriptor which have shown to be efficient in radar target recognition. The first type defines target shape obtained by watershed transformation, facilitating interpretation for the human operator. The second type of vector descriptor is based on so-called polar signatures, obtained using the polar mapping procedure. The latter are highly discriminative and thus significantly improve and facilitate target recognition, leading to greater precision and accuracy. Secondly, we describe the retrieval architecture suitable for the second type of vector descriptor, which checks invariance in relation to target rotation and scale. It is, in itself, more efficient and processing time at the retrieval step is reduced and controlled by the human operator. Finally, in order to validate our proposed feature vectors and architecture, the Support Vector Machine (SVM) classifier will be implemented; the results of which will be tested and presented in the last section of this paper. [Copyright &y& Elsevier]

Abstract: In this paper a novel computation method is proposed to perform the common vector approach (CVA) faster than its conventional implementation in pattern recognition. While conventional CVA calculations perform the classification with respect to the distance between vectors, the new method performs the classification using scalars. A theoretical proof of the equivalence of the proposed method is provided. Next, in order to verify the numerical equivalence of the proposed computation method to the conventional (vector-based) method, numerical experiments are conducted over three different face databases, namely the AR Database, extended Yale Face Database B, and FERET Database. Since the computational gain may depend on (i) the dimension of the feature vectors, (ii) the number of feature vectors used in training, and (iii) the number of classes, the effects of these items are clearly verified via these databases. Our theoretically equivalent (but faster) method provided no difference in the classification rates despite its improved classification speed as compared to the classical implementation of CVA. The new method is found to be about 2.1–3.0 times faster than the conventional CVA implementation for the AR face database, 1.9–3.3 times faster for the extended Yale Face Database B, and 1.9–3.1 times faster for the FERET Database. [ABSTRACT FROM AUTHOR]

Abstract: Rule learning is one of the most common tasks in knowledge discovery. In this paper, we investigate the induction of fuzzy classification rules for data mining purposes, and propose a hybrid genetic algorithm for learning approximate fuzzy rules. A novel niching method is employed to promote coevolution within the population, which enables the algorithm to discover multiple rules by means of a coevolutionary scheme in a single run. In order to improve the quality of the learned rules, a local search method was devised to perform fine-tuning on the offspring generated by genetic operators in each generation. After the GA terminates, a fuzzy classifier is built by extracting a rule set from the final population. The proposed algorithm was tested on datasets from the UCI repository, and the experimental results verify its validity in learning rule sets and comparative advantage over conventional methods. [Copyright &y& Elsevier]