Xiaojuan Zhan

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The classification problem is one of the important research subjects in the field of machine learning. However, most machine learning algorithms train a classifier based on the assumption that the number of training examples of classes is almost equal. When a classifier was trained on imbalanced data, the performance of the classifier declined clearly. For(More)
High-dimensional data and a large number of redundancy features in bioinformatics research have created an urgent need for feature selection. In this paper, a novel random forests-based feature selection method is proposed that adopts the idea of stratifying feature space and combines generalised sequence backward searching and generalised sequence forward(More)
Using data mining technology for disease prediction and diagnosis has become the focus of attention. Data mining technology provides an important means for extracting valuable medical rules hidden in medical data and acts as an important role in disease prediction and clinical diagnosis. This paper surveys some kind of popular data mining techniques for(More)
In this paper, we propose a hybrid of random forest and multivariate adaptive regression splines algorithms for building a breast cancer survivability prediction model. We use random forest to perform a preliminary screening of variables and to receive a importance ranks. Then, the new dataset is extracted from initial WDBC dataset according to top-k(More)
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