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Analysis of gene expression data has emerged as an important approach to discover active pathways related to biological phenotypes. Previous pathway analysis methods use all genes in a pathway for linking it to a particular phenotype. Using only a subset of informative genes, however, could better classify samples. Here, we propose a two-stage machine learning approach for pathway analysis. During...
This paper presents a dynamical decision method derived from ensemble decision method. It is designed to be robust with respect to abrupt change of sensor response. Abrupt change may be caused by impulsive noise, sensor degradation or transmission fault in the case of an autonomous sensor network. It can also be caused by inconsistency of sensor responses due to local or sudden break of one monitored...
In this paper, a two-stage off-line signature verification system based on dissimilarity representation is proposed. In the first stage, a set of discrete left-to-right HMMs trained with different number of states and codebook sizes is used to measure similarity values that populate new feature vectors. Then, these vectors are input to the second stage, which provides the final classification. Experiments...
This paper proposes an image semantic classification algorithm based on feature subspaces. It is implemented by SVM and AdaBoost algorithm. In every feature subspace, a SVM is trained. According to the error rate of every SVM, the integrating weight of feature subspace is determined, with which different subspace features are concatenated into a feature vector. Then AdaBoost algorithm is employed...
This paper presents a study on using a concept feature to detect web phishing problem. Following the features introduced in Carnegie Mellon Anti-phishing and Network Analysis Tool (CANTINA), we applied additional domain top-page similarity feature to a machine learning based phishing detection system. We preliminarily experimented with a small set of 200 web data, consisting of 100 phishing webs and...
In this paper we suggest an approach to select features for the support vector machines (SVM). Feature selection is efficient in searching the most descriptive features which would contribute in increasing the effectiveness of the classifier algorithm. The process described here consists in backward elimination strategy based on the criterion of the rate of misclassification. We used the tabu algorithm...
In this article, we present a contribution to the violent Web images classification. This subject is deeply important as it has a potential use for many applications such as violent Web sites filtering. We propose to combine the techniques of image analysis and data-mining to relate low level characteristics extracted from the image's colors to a higher characteristic of violence which could be contained...
The analysis of microarray data is a challenging task for statistical and machine learning methods, since the datasets usually contain a very large number of features (genes) and only a small number of examples (subjects). In this work, we describe a technique for gene selection and classification of microarray data based on the recently proposed potential support vector machine (P-SVM) for feature...
We describe how we were able to improve the accuracy of a medium-vocabulary spoken dialog system by rescoring the list of n-best recognition hypotheses using a combination of acoustic, syntactic, semantic and discourse information. The non-acoustic features are extracted from different intermediate processing results produced by the natural language processing module, and automatically filtered. We...
Notice of Violation of IEEE Publication Principles"An Approach to Recognize Handwritten Bengali Numerals for Postal Automation"by Md. Saidur Rahman, G. M. Atiqur Rahaman, Asif Ahmed and G. M. Salahuddinin the Proceedings of 11th International Conference on Computer and Information Technology (ICCIT 2008, 25-27 December, 2008, Khulna, Bangladesh)After careful and considered review of the...
Novel feature-selection methods are proposed for multi-class support-vector-machine (SVM) learning. They are based on two new feature-ranking criteria. Both criteria, collectively termed multi-class feature-based sensitivity of posterior probabilities (MFSPP), evaluate the importance of a feature by computing the aggregate value, over the feature space, of the absolute difference of the probabilistic...
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