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kNN is extensively studied for multilabel classification in the literature. Several kNN-based multilabel learning algorithms have been witnessed during the past years. They usually take kNN as their base classifiers to construct classification models, and then predict the class labels by virtue of Bayesian or majority rules. In this paper, a nearest neighbor selection for multilabel classification...
k nearest neighbor (kNN) is an effective and powerful lazy learning algorithm, notwithstanding its easy-to-implement. However, its performance heavily relies on the quality of training data. Due to many complex real-applications, noises coming from various possible sources are often prevalent in large scale databases. How to eliminate anomalies and improve the quality of data is still a challenge...
kNN is a simple, but effective and powerful lazy learning algorithm. It has been now widely used in practice and plays an important role in pattern classification. However, how to choose an optimal value of k is still a challenge, which straightforwardly affects the performance of kNN. To alleviate this problem, in this paper we propose a new learning algorithm under the framework of kNN. The primary...
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