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In this paper we propose a novel Support Vector Machine(SVM) based approach for noisy data removal from datasets. It is observed that the instability present in the dataset greatly affects the overall performance of the any classifier. Hence, we propose a methodology for removal of such instabilities. In the proposed approach, we proceed by determining the clusters formed using support equilibrium...
This paper analyzes the existing decision tree classification algorithms based on variable precision rough set and finds that these algorithms have better classification accuracies and can tolerate the noise data. But when choosing the best attribute using variable precision rough set, these algorithms still have the shortages in ID3. That is, these algorithms also tend to choose the attribute with...
The problem of classification on highly imbalanced datasets has been studied extensively in the literature. Most classifiers show significant deterioration in performance when dealing with skewed datasets. In this paper, we first examine the underlying reasons for SVM's deterioration on imbalanced datasets. We then propose two modifications for the soft margin SVM, where we change or add constraints...
The sequential data flux in many time-series applications require that only a small fraction of the data are stored for future processing. Furthermore, labels for these data are possibly sparse and they might show some biases. To support learning under such restrictive constraints, we combine manifold regularization with sequential learning under a semi-supervised learning scenario. The online learning...
One of disadvantages of Hidden Markov Models (HMMs) is its low resistance to unexpected noises among observation sequences. Unexpected noises in a sequence usually ??break?? a sequence of observations, and then makes this sequence unrecognizable for trained models. We propose a new HMM training and testing scheme, which compensates some of the negative effects of such noises. We carried out experiment...
The fuzzy support vector machines (FSVMs) can be used to deal with multiclass classification problems where the key issue is to solve a quadratic programming problem. This paper introduces a new fuzzy multiclass support vector machines (FMSVMs) based on compact description of data, which extends the exiting support vector machine method to the case of k-class problem in one optimization task (quadratic...
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