Abstract-Traditional classification algorithms, in many times, perform poorly on imbalanced data sets in which some classes are heavily outnumbered by the remaining classes. For this kind of data, minority class instances, which are usually much more of interest, are often misclassified. The paper proposes a method to deal with them by changing class distribution through oversampling at the borderline between the minority class and the majority class of the data set. A Support Vector Machines (SVMs) classifier then is trained to predict new unknown instances. Compared to other over-sampling methods, the proposed method focuses only on the minority class instances lying around the borderline due to the fact that this area is most crucial for establishing the decision boundary. Furthermore, new instances will be generated in such a manner that minority class area will be expanded further toward the side of the majority class at the places where there appear few majority class instances. Experimental results show that the proposed method can achieve better performance than some other over-sampling methods, especially with data sets having low degree of overlap due to its ability of expanding minority class area in such cases.

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Sampling is the most popular approach for handling the class imbalance problem in training data. A number of studies have recently adapted sampling techniques for dynamic learning settings in which the training set is not fixed, but gradually grows over time. This paper presents an empirical study to compare over-sampling and under-sampling techniques in the context of data streaming. Experimental results show that undersampling performs better than over-sampling at smaller training set sizes. All sampling techniques, however, are comparable when the training set becomes larger. This study also suggests that a multiple random under-sampling (MRUS) technique should be a good choice for applications with imbalanced and streaming data, because MRUS is the most effective while still keeping a high speed.

Townscape colours have been a main factor in urban development. For townscape colours, keeping colour harmony within the environment is a common goal. Expressing characteristics and impressions of the town in townscape colours are other meaningful goals. The colour planning support system proposed here is intended to improve townscapes. The system offers some colour combination proposals based on three elements: colour harmony, impressions of the townscape, and cost for the change of colours. The objective of the present paper is to construct the colour harmony and Kansei evaluation models that evaluate colour combinations in the colour planning support system. The colour harmony equations by Moon and Spencer are employed for the construction of the colour harmony model. The Kansei model, which quantifies the impressions of the townscape, is constructed from the approach of Kansei engineering with neural networks. After the construction, evaluation experiments are conducted for 20 subjects to test the performance of both models. The results of the tests show sufficient correlation between model output and subject response for each model.

The study presented explores the extent to which tactile stimuli delivered to the ten digits of a BCI-naive subject can serve as a platform for a brain computer interface (BCI) that could be used in an interactive application such as robotic vehicle operation. The ten fingertips are used to evoke somatosensory brain responses, thus defining a tactile brain computer interface (tBCI). Experimental results on subjects performing online (real-time) tBCI, using stimuli with a moderately fast inter-stimulus-interval (ISI), provide a validation of the tBCI prototype, while the feasibility of the concept is illuminated through information-transfer rates obtained through the case study.