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Abstract-Prediction of protein-proteininteraction sites is very important to the function of a protein and drug design. In this paper, we adequately utilize the characters of ensemble learning, which can improve the accuracy of individual classifier and generalization ability of the system, and propose a new prediction method of protein-protein interaction sites: ensemble learning method based on...
Ensemble learning is a commonly used tool for building prediction models from data streams, due to its intrinsic merits of handling large volumes stream data. Despite of its extraordinary successes in stream data mining, existing ensemble models, in stream data environments, mainly fall into the ensemble classifiers category, without realizing that building classifiers requires labor intensive labeling...
Recently, it has been shown that, in ensemble learning, it may be preferable to ensemble some instead of all the classifiers. Various selective ensemble approaches are then designed, where optimization algorithms like genetic algorithm (GA) are used to evolve weights of component classifiers and classifiers with weights greater than a threshold are selected. This paper proposes a novel selective ensemble...
In this paper, we propose KNC algorithm for combining KNN algorithm and other three classifiers (C4.5 algorithm, Naive Bayes classifier and SVM) based on their classification capabilities on different types of instances. According to labels of instances and their K nearest neighbors, we divide instances into three types, S-, DS- and D-type. The classification capabilities of KNN algorithm on S-type...
Data mining techniques, especially classification methods, are receiving increasing attention from researchers and practitioners in the domain of petroleum exploration and production (E&P) in China. To extensively investigate the effects of feature selection and learning algorithms on the hydrocarbon reservoir prediction performance, taking three real-world multiclass problems as examples, namely...
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