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Classification of data points in a data stream is a fundamentally different set of challenges than data mining on static data. While streaming data is often placed into the context of "Big Data" (or more specifically "Fast Data") wherein one-pass algorithms are used, true data streams offer additional hurdles due to their dynamic, evolving, and non-stationary nature. During the...
Classification using association is a recent data mining approach that integrates association rule discovery and classification. A modified version of the Multi-class Classification based on Association Rule (MCAR) is proposed in this paper. The proposed classifier, known as Modified Multi-class Classification based on Association Rule, MMCAR, employs a new rule production function which resulted...
Besides high accuracy, stability of feature selection has recently attracted strong interest in knowledge discovery from high-dimensional data. In this study, we present a theoretical framework about the relationship between the stability and accuracy of feature selection based on a formal bias-variance decomposition of feature selection error. The framework also suggests a variance reduction approach...
Traditional feature selection algorithms require a large number of labeled training instances to find out the most informative subset of features. However, in many real-world applications, the labeled data are often difficult, expensive or time-consuming to obtain. Recently, several semi-supervised feature selection algorithms were proposed, which aim at doing feature selection with the help of some...
In the past we have seen various developments in the philosophy and application of neural networks. We today have backpropagation algorithm, Hopfield networks, perceptrons, etc. All these are very precise tools which model the data very well. But unfortunately, the problem being faced these days is of training the neural network in short span of time, over the test data. All the above mentioned tools...
Genetic programming (GP), is a very general and efficient technique, often capable of outperforming more specialized techniques on a variety of tasks. In this paper, we suggest a straightforward novel algorithm for post-processing of GP classification trees. The algorithm iteratively, one node at a time, searches for possible modifications that would result in higher accuracy. More specifically, the...
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