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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...
The Covering algorithm is proposed by Professor ZhangLing and ZhangBo in the 20th century, which simulates the structure of human learning, building a Constructive Neural Network Learning Model. Covering algorithm has been widely used to solve massive data classification problem, because its performance. The covering classification algorithm has fast learning, high recognition rate, massive data processing...
The class imbalance problem usually occurs in real applications. The class imbalance is that the amount of one class may be much less than that of another in training set. Under-sampling is a very popular approach to deal with this problem. Under-sampling approach is very efficient, it only using a subset of the majority class. The drawback of under-sampling is that it throws away many potentially...
The identification of protein-protein interface residues is essential for drug design, understanding cell activity of organism. In this paper, a couple of covering algorithms are presented to predict protein-protein interaction sites by using several protein features, such as sequence profile, residue entropy and so on. These features are utilized to construct covering algorithms classifiers to identify...
Probabilistic Neural Networks (PNN) learn quickly from examples in one pass and asymptotically achieve the Bayes-optimal decision boundaries. The major disadvantage of PNN is that it requires one node or neuron for each training sample. Various clustering techniques have been proposed to reduce this requirement to one node per cluster center. A new fast optimization of PNN is investigated here using...
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