Papers by Keyword: Selective Ensemble

Abstract: In this paper we intend to apply a new method to predict tertiary structure. Several feature extraction methods adopted are physicochemical composition, recurrence quantification analysis (RQA) , pseudo amino acid composition (PseAA) and Distance frequency. We construct the binary tree Classification model, and adopt flexible neural tree models as the classifiers. We will train a number of based classifiers through different features extraction methods for every node of binary tree, then employ the selective ensemble method to ensemble them. 640 dataset is selected to our experiment. The predict accuracy with our method on this data set is 63.58%, higher than some other methods on the 640 datasets. So, our method is feasible and effective in some extent.

Abstract: It is valuable for supply chain management to analyze and evaluate the operation performance of supply chain, and use timely and accurate operation state feedback to adjust the system to keep smooth running. Aiming at the complexity and uncertainty of supply chain, this thesis put forward a heterogeneous selective ensemble principle component analysis (PCA) algorithm based on fuzzy integral via Bagging ensemble learning. Besides, by using dimension reduction on high-dimensional data set, it realizes to extract the key factors of supply chain performance and breaks the bottleneck that problems in supply chain management cannot be recognized rapidly or analyzed by traditional performance evaluation method. According to the empirical research on survey data of supply chain performance at C Group, the algorithm is proved be effective.

Abstract: Universal steganalysis include feature extraction and steganalyzer design. Most universal steganalysis use Support Vector Machine (SVM) as steganalyzer. However, most SVM-based universal steganalysis are not to be very much effective at lower embedding rates. The reason why selective SVMs ensemble improve the generalization ability was analyzed, and an algorithm to select a part of individual SVMs according to their difference to build the ensemble classifier was proposed, which based on the selected ensemble theory-Many could be better than all. In this paper, the selective SVMs ensemble algorithm was used to construct a strong steganalyzer to improve the performance of steganographic detection. The twenty five experiments on the benchmark with 2000 different types of images show that: for popular steganography methods, and under different conditions of embedding rate, the average detection rate of proposed steganalysis method outperforms the maximum average detection rate for the steganalysis method based on single SVM with improving by 3.05%-12.05%; and for the steganalysis method based on BaggingSVM with improving by 0.2%-1.3%.

Abstract: The IT performance evaluation is a complex analysis system. Some key factors that can reflect the essential characteristics and be easy to acquire, should be extracted to help us to clarify evaluation, analysis and diagnoses. In this paper for the uncertainty of IT performance data, an algorithm of IT performance key factors extraction based on selective ensemble of principal component analysis (PCA) is proposed. Unlike the traditional PCA algorithm, PCA selective ensemble increase the difference degree between the main component and the others. Moreover it expands the selective ensemble in the application of unsupervised algorithm. The validity of the algorithm is verified through the analysis of the actual data.

Abstract: In ensemble learning, in order to improve the performance of individual classifiers and the diversity of classifiers, from the classifiers generation and combination, this paper proposes a kind of combination feature division and diversity measure of multi-classifier selective ensemble algorithm. The algorithm firstly applied bagging method to create some feature subsets, Secondly using principal component analysis of feature extraction method on each feature subsets, then select classifiers with high-classification accuracy; finally before classifier combination we use classifier diversity measure method select diversity classifiers. Experimental results prove that classification accuracy of the algorithm is obviously higher than popular bagging algorithm.