Eric W. Cooper

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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(More)
Learning from imbalanced data has conventionally been conducted on stationary data sets. Recently, there have been several methods proposed for mining imbalanced data streams, in which training data is read in consecutive data chunks. Each data chunk is considered as a conventional imbalanced data set, making it easy to apply sampling methods to balance(More)
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(More)
In this paper, we present a computing model for diagnosis and prescription in oriental medicine. Inputs to the model are severities of symptoms observed on patients and outputs from the model are a diagnosis of disease states and treatment herbal prescriptions. First, having used rule inference with a Gaussian distribution, the most serious disease state in(More)
Collaborative Decision Making (CDM) is one of the concepts of human reasoning awareness, which refers to expert knowledge of the group and its preferences in a dynamic market environment. In this paper, we present a new approach, which is a framework for collaborative decision making, together with expert feelings about market dynamics to deal with multiple(More)
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(More)
In this paper, we propose a Hybrid Kansei-SOM model, using Kansei Evaluation integrated with Self-Organizing Map (SOM) for stock market investment strategies. The proposed approach, using a group Decision Support System (DSS), aims to aggregate experts’ preferences with the selection of the most suitable stocks, matching with investing strategies to achieve(More)