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We present a novel method for fusing the decisions of multiple classification algorithms which use different features, classification methods, and data sources. The proposed method, called context dependent fusion of multiple algorithms (CDF-MA) is motivated by the fact that the relative performance of different algorithms can vary significantly as the characteristics of the input data vary. The training...
In this paper, we derive a maximum a posteriori (MAP) classifier using the features extracted by biased discriminant analysis (BDA) in multi-class classification problems. Using the one-against-the-rest scheme we construct several feature spaces, where the MAP classifier is formulated. Although the maximum likelihood (ML) classifier is generally equivalent to the MAP classifier when the prior probability...
When building topic based document classifiers, feature selection is a key step: features not holding any information about the topic of a document introduce only unnecessary noise during the classification. In a distributed environment, when the nodes are interacting, the locally retrieved features and the their attributes must be shared to have at every node a more accurate estimation of the global...
A novel classifier-independent feature selection algorithm based on the posterior probability is proposed for imbalanced datasets. First, an imbalanced factor is introduced and computed by Parzen-window estimation. The middle point of Tomek links is chosen as the initial point. Accordingly, this algorithm is iterated to find out the boundary points which have the equality of posterior probability...
Human body is a natural emitter of infrared ray. Usually the body temperature is different from that of surrounding. This leads to gray-scale difference between human body and background in infrared thermal image. So the method of infrared thermal imaging was presented to classify gaits. Firstly, infrared videos of 23 subjects were collected by using infrared thermal camera. Body silhouettes were...
This work proposes a fast decision algorithm in pattern classification based on Gaussian mixture models (GMM). Statistical pattern classification problems often meet a situation that comparison between probabilities is obvious and involve redundant computations. When GMM is adopted for the probability model, the exponential function should be evaluated. This work firstly reduces the exponential computations...
Clustering techniques have been used by many intelligent software agents in order to retrieve, filter, and categorize documents available on the World Wide Web. Clustering is also useful in extracting salient features of related Web documents to automatically formulate queries and search for other similar documents on the Web. Traditional clustering algorithms either use a priori knowledge of document...
This paper describes the evaluation of a hierarchical classifier for classifying multi-labeled documents organized in a two-level taxonomy. The hierarchical classifier consists of a tree of independent naive Bayes classifiers, with output probabilities from parent classifiers propagated to child classifiers as additional features. Each classifier uses Bi-Normal Feature Separation for word feature...
Data classification has been studied widely in the fields of Artificial Intelligence, Machine Learning, Data Mining and Pattern Recognition. Up to the present, the development of classification has made great achievements, and many kinds of classified technology and theory will continue to emerge. This paper discusses a great deal of classification algorithms based on the Artificial Neural Networks,...
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...
Product feature extraction is an important task of review mining and summarization. The task of product feature extraction is to find product features that customers refer to in their topic reviews. It would be useful to characterize the opinions which they review or express about the products. In this paper, we propose an approach to product feature extraction using a maximum entropy model. Maximum...
This paper addresses the problem of probability estimation in multiclass classification tasks combining two well known data mining techniques: support vector machines and neural networks. We present an algorithm which uses both techniques in a two-step procedure. The first step employs support vector machines within a one-vs-all reduction from multiclass to binary approach to obtain the distances...
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