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Symbolic time series analysis (STSA) is built upon the concept of symbolic dynamics that deals with discretization of dynamical systems in both space and time. The notion of STSA has led to the development of a pattern recognition tool in the paradigm of dynamic data-driven application systems (DDDAS), where a time series of sensor signals is partitioned to obtain a symbol sequence that, in turn,...
Quantification is the name given to a novel machine learning task which deals with correctly estimating the number of elements of one class in a set of examples. The output of a quantifier is a real value, since training instances are the same as a classification problem, a natural approach is to train a classifier and to derive a quantifier from it. Some previous works have shown that just classifying...
Aiming to increase the proportion of the samples that has been determinate classified in Naive Credal Classifier, this paper improves conservative inference rule and proposes an incomplete data classification model based on relaxed conservative inference rule. Simulation results of comparative experiment with Naive Bayesian Classifier and Naive Credal Classifier verify the effectiveness of this classification...
This paper presents Perturbed Frequent Itemset based Classification Technique (PERFICT), a novel associative classification approach based on perturbed frequent itemsets. Most of the existing associative classifiers work well on transactional data where each record contains a set of boolean items. They are not very effective in general for relational data that typically contains real valued attributes...
Uncontrolled project investment attracts more and more public attention. The inaccuracy of cost estimation is one of main reasons that make the government investment out of control. Cost estimation is affected by many uncertain factors, and the relationship between these factors are nonlinear, and the traditional model is hard to solve. This paper brings forward a model based on rough set and neural...
Accuracy is a very important criterion for the classifier in the process of classification. In this paper, a unified paradigm for the calculation of accuracy evaluated different classifier, using topological covering-based granular computing, is presented under the given sample space and different ideal classification assumptions. And corresponding examples for the calculation of accuracy in different...
This paper addresses the multimodal nature of social dominance and presents multimodal fusion techniques to combine audio and visual nonverbal cues for dominance estimation in small group conversations. We combine the two modalities both at the feature extraction level and at the classifier level via score and rank level fusion. The classification is done by a simple rule-based estimator. We perform...
The paper studies three typical weighting strategies for Shell-Neighbor Imputation (SNI) algorithm, while there are many weighting modes that can be used in the SNI. To best capture the imputation efficiency, a new metrics, called goodess, is proposed for evaluating imputation algorithms. We conduct some experiments for examining the proposed approached, and demonstrate that (1) distance-frequency-weighting...
As more and more multimedia data become available on the Web, mining on those data is playing an increasingly important role in Web applications. In this paper, we investigate the interplay between multimedia data mining and text data mining. Specifically, in an approach we called text-aided image classification (TAIC), we address the problem of image classification with very limited amount of labeled...
The recently introduced theory of compressive sensing enables the recovery of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can be much smaller than the number of Nyquist-rate samples. Interestingly, it has been shown that random projections are a near-optimal measurement scheme. This has inspired the design of hardware...
It is well known that the key of Bayesian classifier learning is to balance the two important issues, that is, the exploration of attribute dependencies in high orders for ensuring a sufficient flexibility in approximating the ground-truth dependencies, and the exploration of low orders for ensuring a stable probability estimate from limited training samples. By allowing one-order attribute dependencies,...
Representation learning is a fundamental challenge for feature selection and plays an important role in applications such as dimension reduction, data mining and object recognition. Traditional linear representation methods, such as principal component analysis (PCA), independent component analysis (ICA) and linear discriminate analysis (LDA), have good performance on certain applications based on...
In this paper, a new decision tree construction algorithm (MIDT) is proposed. MIDT (Multiple Informative Decision Tree) uses principal component analysis to integrate information gain, samples distribution information and correlation coefficient as the basis of the selection of splitting attributes. This method can overcome the disadvantage of ID3 decision tree construction method that uses information...
Naive Bayes Classifiers have been known with the advantages of high efficiency and good classification accuracy and they have been widely used in many domains. However, the classifiers need complete data. And the phenomenon of missing data widely exists in practice. Facing this instance, learning naive Bayes classifier and classification method with missing data are built in this paper. Compared with...
Illumination estimation for color constancy is an important problem in computer vision. Existing algorithms can be divided into two groups: physics-based algorithms and statistics-based approaches. In this paper, the advantages of the two kinds are integrated. At first, a novel statistic-based algorithm called Illumination Estimation using K-nearest-neighbor (IE-KNN) is proposed. And then the physics-based...
The method of a black-box diagnostics, founded on nonparametric identification of objects using integro-power Volterra series is offered. It provides a set of diagnostic features formed on base of multidimensional Volterra kernels: discrete values of Volterra kernels, heuristic features, moments and wavelet transform coefficients. It is researched a self-descriptiveness of provided features using...
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...
The area under the Receiver Operating Characteristic curve (AUC) has been successfully applied to binary-class tasks. However, its extension to multi-class problems has become a difficult task due to some practical issues. Up to now, its generalization work is relatively little and is not considerably ideal. In this paper, a new method is presented to estimate AUC for multi-class problems, which not...
One of the most used intelligent technique for classification is a neural network. In real classification applications the patterns of different classes often overlap. In this situation the most appropriate classifier is the one whose outputs represent the class conditional probabilities. These probabilities are calculated in traditional statistics in two steps: first the underlying prior probabilities...
A discriminative training algorithm to estimate continuous-density hidden Markov model (CDHMM) for automatic speech recognition is considered. The algorithm is based on the criterion, called margin-enhanced maximum mutual information (MEMMI), and it estimates the CDHMM parameters by maximizing the weighted sum of the maximum mutual information objective function and the large margin objective function...
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