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For many data mining applications, it is necessary to develop algorithms that use unlabeled data to improve the accuracy of the supervised learning. Co-Training is a popular semi-supervised learning algorithm. It assumes that each example is represented by two or more redundantly sufficient sets of features (views) and these views are independent given the class. However, these assumptions are not...
Emotion recognition is an important module in affective computing. It is usually studied based on facial and audio information with methodologies such as ANN, fuzzy set, SVM, HMM, etc. In this paper, a novel approach based on selective ensemble is proposed for emotion recognition. Simulation experiments prove that the proposed method has better performance than the method of single classifier, even...
A novel ensemble learning algorithm based on discretization method is proposed in this paper. This algorithm uses the rough sets and Boolean reasoning approach to construct base classifiers with good diversity, which can improve the performance of ensemble learning. Then the consistency level coined from the rough sets theory is utilized to measure the information loss and control the algorithmpsilas...
Sensory evaluation is one of the key steps in recipe product design. With the development of compute intelligence technology, many methods such as artificial neural network, decision tree, regression, etc are used to solve the problems in sensory evaluation. This becomes more and more popular. But the generalization ability using single model needs to be improved. This paper uses bagging algorithm...
Aiming at diversity being a necessary condition of the ensemble learning, we study method for improving diversity of the neural networks ensemble based on K-means clustering technique. In this paper, we propose a selecting approach that is first to train many classifiers through training set with neural network algorithm, and to classify data on validation set using classifiers. And then we use the...
A new microcalcification clusters (MCs) detection method in mammograms is proposed in this paper, which is based on a new ensemble learning method. The ground truth of MCs is assumed to be known as a priori. In our algorithm, each MCs is enhanced by a well designed high-pass filter. Then the 116 dimensional image features are extracted by the feature extractor and fed to the ensemble decision model...
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