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K-Nearest-Neighbor (KNN) as an important classification method based on closest training examples has been widely used in data mining due to its simplicity, effectiveness, and robustness. However, the class probability estimation, the neighborhood size and the type of distance function confronting KNN may affect its classification accuracy. Many researchers have been focused on improving the accuracy...
The majority of machine learning algorithms previously designed usually assume that their training sets are well-balanced, and implicitly assume that all misclassification errors cost equally. But data in real-world is usually imbalanced. The class imbalance problem is pervasive and ubiquitous, causing trouble to a large segment of the data mining community. The tradition machine learning algorithms...
KNN (k-nearest-neighbor) has been widely used as an effective classification model. In this paper, we summarize three main shortcomings confronting KNN and single out three main methods for overcoming its three shortcomings. Keeping to these methods, we try our best to survey some improved algorithms and experimentally tested their effectiveness. Besides, we discuss some directions for future study...
Numerous approaches have been proposed to improve the classification accuracy of Naive Bayes by weakening the attribute independence assumption. To maintain the simple structure and low computational cost, many researches focus on the one-dependence estimator. In this paper, we present a novel algorithm called one-dependence estimator based on multi-parents (MPODE). In MPODE, each attribute has multi-parents;...
The naive Bayesian classifier provides a very simple and effective model for machine learning, but its attribute independence assumption is often violated in the real world. To improve the performance of Bayesian classifier, we present a novel algorithm called evolutional one-dependence augmented naive Bayes (EANB), which selects the attributes' parents by carrying an evolutional search through the...
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