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In this paper we propose efficient color segmentation method which is based on the Support Vector Machine classifier operating in a one-class mode. The method has been developed especially for the road signs recognition system, although it can be used in other applications. The main advantage of the proposed method comes from the fact that the segmentation of characteristic colors is performed not in the original but in the higher dimensional feature space. By this a better data encapsulation with a linear hypersphere can be usually achieved. Moreover, the classifier does not try to capture the whole distribution of the input data which is often difficult to achieve. Instead, the characteristic data samples, called support vectors, are selected which allow construction of the tightest hypersphere that encloses majority of the input data. Then classification of a test data simply consists in a measurement of its distance to a centre of the found hypersphere. The experimental results show high accuracy and speed of the proposed method.

The presence of noise, loss of information or feature nonstationarity in data is the limiting factor for many machine learning decision systems. Previous research has shown that relevant feature selection may be helpful to alleviate the impact of these possible perturbations. This paper presents a dynamical feature subspaces selection method based on ensembles of one-class Support Vector Machine (SVM), with the objective to optimize the performance of a decision system in such a nonstationary environment. Our method is predicated on the assumption that only the performance of the classifiers using perturbed features is degraded. We propose a mechanism for constructing an ensemble of classifiers based on a large number of feature subspaces generated from the initial full-dimensional space. In the phase of classification, the ensemble system is capable to select adaptively feature subspaces which are supposed to be immune to the nonstationary disturbance and to make the final decision by combining the individual decisions of classifiers built in these subspaces. One characteristic of this method is that we use the one-class SVM ensemble to accomplish simultaneously the tasks of feature subspace selection and classification. The effectiveness of the proposed method has been demonstrated through the experiments conducted in the context of textured image classification.

Band selection plays a key role in the hyperspectral image classification since it helps to reduce the expensive cost of computation and storage. In this paper, we propose a supervised hyperspectral band selection method based on differential weights, which depict the contribution degree of each band for classification. The differential weights are obtained in the training stage by calculating the sum of weight differences between positive and negative classes. Using the effective one-class Support Vector Machine (SVM), the bands corresponding to large differential weights are extracted as discriminative features to make the classification decision. Moreover, label information from training data is further exploited to enhance the classification performance. Finally, experiments on three public datasets, as well as comparison with other popular feature selection methods, are carried out to validate the proposed method.

Genomic islands (GIs) are clusters of functionally related genes acquired by lateral genetic transfer (LGT), and they are present in many bacterial genomes. GIs are extremely important for bacterial research, because they not only promote genome evolution but also contain genes that enhance adaption and enable antibiotic resistance. Many methods have been proposed to predict GI. But most of them rely on either annotations or comparisons with other closely related genomes. Hence these methods cannot be easily applied to new genomes. As the number of newly sequenced bacterial genomes rapidly increases, there is a need for methods to detect GI based solely on sequences of a single genome. In this paper, we propose a novel method, GI-SVM, to predict GIs given only the unannotated genome sequence. GI-SVM is based on one-class support vector machine (SVM), utilizing composition bias in terms of k-mer content. From our evaluations on three real genomes, GI-SVM can achieve higher recall compared with current methods, without much loss of precision. Besides, GI-SVM allows flexible parameter tuning to get optimal results for each genome. In short, GI-SVM provides a more sensitive method for researchers interested in a first-pass detection of GI in newly sequenced genomes.