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Common stream mining tasks include classification, clustering and frequent pattern mining among them, data stream classification has drawn particular attention due to its vast real-time application. Through these applications, the main goal is to efficiently build classification models from data streams for accurate prediction. The development of such model has shown the need for machine learning...
MicroRNAs are one type of noncoding RNA that regulate their target mRNAs before mRNAs are translated into proteins. Although it has been demonstrated that the regulation is through partial binding of the seed region of a miRNA and its targets, the mechanism of this process is not fully discovered. Some biological experiments have shown that even perfect base pairing in the seed region does not always...
The county level of basic public services analysis and classification play an important role in county economic growth and improve benefit of healthy development of urbanization in China. According to the county level of basic public services data which is large scale and imbalance, this paper presented a support vector machine model to classify the county level of basic public services. The method...
Individual credit risk evaluation is an important and challenging data mining problem in financial analysis domain. This paper compares the effectiveness of four data mining algorithms - logistic regression (LR), decision tree (C4.5), support vector machine (SVM) and neural networks (NN) by applying them to two credit data sets. Experiment results show that the LR and SVM algorithms produced the best...
Rough set theory (RST), support vector machine (SVM), and decision tree (DT) are brightly data mining methodologies for classification prediction tasks. While the accuracy for class prediction is highly emphasized, the ability to generate rules for decision support is also important in some practical applications. Studies have shown the ability of RST for feature selection while SVM and DT are significantly...
Various benchmarking studies have shown that artificial neural networks and support vector machines often have superior performance when compared to more traditional machine learning techniques. The main resistance against these newer techniques is based on their lack of interpretability: it is difficult for the human analyst to understand the reasoning behind these models' decisions. Various rule...
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