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In this paper, a modified XCS is proposed to reduce the numbers of learned rules. XCS is a type of learning classifier systems and has been proven able to find accurate, maximal generalizations. However, XCS usually produces too many rules such that the readability of the classification model is greatly reduced. As a result, XCS users may not be able to obtain the desired knowledge or useful information...
Financial forecasting is a lucrative and complicated application of machine learning. In this paper, we focus on the finding investment opportunities. We therefore explore four different Genetic Programming approaches and compare their performances on real-world data. We find that the novelties we introduced in some of these approaches indeed improve the results. However, we also show that the Genetic...
This paper aims to challenge the problem of finding accurate and relevant rules for the task of classification. The scope is to improve the accuracy, or at least to provide a comparable accuracy measure, for classification algorithms implemented so far. Because the task of classification must be as accurate as possible, the paper proposes a method based on genetic algorithms to enhance the speed and...
With the development and widely used of Internet and information technology, the Web has become one of the most important means to obtain information for people. According to the Web document classification and the theory of artificial neural network, a Web classification mining method based on classify support vector machine (SVM) is presented in this paper. The SVM network structure that used for...
The k-nearest neighbor(k-NN) is improved by applying rough set and distance functions with relearning and ensemble computations to classify data with the higher accuracy values. Then, the proposed relearning and combining ensemble computations are an effective technique for improving accuracy. We develop a new approach to combine kNN classifier based on rough set and distance functions with relearning...
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