Kyriacos Chrysostomou

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Interactive multimedia learning systems use sophisticated techniques to present advanced interface features. However, not all users appreciate the strengths of such interface features because of the variations of user backgrounds and skills. In this context, human factors are important issues in deciding user preferences. This study applies a data mining(More)
Wrapper feature selection methods are widely used to select relevant features. However, wrappers only use a single classifier. The downside to this approach is that each classifier will have its own biases and will therefore select very different features. In order to overcome the biases of individual classifiers, this study introduces a new data mining(More)
INTRODUCTION It is well known that the performance of most data mining algorithms can be deteriorated by features that do not add any value to learning tasks. Feature selection can be used to limit the effects of such features by seeking only the relevant subset from the original features (de Souza et al., 2006). This subset of the relevant features is(More)
Many organisations, nowadays, have developed their own databases, in which a large amount of valuable information, e.g., customers’ personal profiles, is stored. Such information plays an important role in organisations’ development processes as it can help them gain a better understanding of customers’ needs. To effectively extract such information and(More)
In this paper we have made a review of various outlier detection techniques from data mining perspective. Existing studies in data mining focus generally on finding patterns from large datasets and using it for organizational decision making. However, finding exceptions and outliers did not receive much attention in the data mining field as other topics(More)
Wrapper feature selection approaches are widely used to select a small subset of relevant features from a dataset. However, Wrappers suffer from the fact that they only use a single classifier when selecting the features. The downside to this approach is that each classifier will have its own biases and will therefore select very different features. In(More)
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