We introduce the definition of fuzzy co-transform, as complementary to the definition of fuzzy transform and applied to co-domain of real functions. Basic properties of direct and inverse fuzzy co-transform are discus...
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We introduce the definition of fuzzy co-transform, as complementary to the definition of fuzzy transform and applied to co-domain of real functions. Basic properties of direct and inverse fuzzy co-transform are discussed. Examples of application to time series are given.
We propose a possibility theory-based approach to the treatment of missing user preferences in skyline queries. To compensate this lack of knowledge, we show how a set of plausible preferences suitable for the current...
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We propose a possibility theory-based approach to the treatment of missing user preferences in skyline queries. To compensate this lack of knowledge, we show how a set of plausible preferences suitable for the current context can be derived either in a case-based reasoning manner, or using an extended possibilistic logic setting. Uncertain dominance relationships are defined in a possibilistic way and the notion of possibilistic contextual skyline is introduced. This kind of skyline allows us to return the tuples that are non-dominated with a high certainty. The paper also includes a structured overview of the different types of “fuzzy” skylines.
This paper investigates the effect of diversity caused by Negative Correlation Learning (NCL) in the combination of neural classifiers and presents an efficient way to improve combining performance. Decision Templates...
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This paper investigates the effect of diversity caused by Negative Correlation Learning (NCL) in the combination of neural classifiers and presents an efficient way to improve combining performance. Decision Templates and Averaging, as two non-trainable combining methods and Stacked Generalization as a trainable combiner are investigated in our experiments. Utilizing NCL for diversifying the base classifiers leads to significantly better results in all employed combining methods. Experimental results on five datasets from UCI repository indicate that by employing NCL, the performance of the ensemble structure can be more favorable compared to that of an ensemble use independent base classifiers.
Objects are usually described by combinations of properties. Logic-based descriptions offer compact representations for binary properties. Besides, Sugeno integrals are well-known as a powerful qualitative aggregation...
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Objects are usually described by combinations of properties. Logic-based descriptions offer compact representations for binary properties. Besides, Sugeno integrals are well-known as a powerful qualitative aggregation tool in multiple-criteria decision, which is applicable to gradual properties, and takes into account positive synergies between properties. The paper proposes to investigate the potential use of Sugeno integrals as a representation tool, to lay bare their relation with possibilistic logic representations, and to discuss the handling of negative synergies in this setting using a pair of Sugeno integrals.
Real-time feature extraction is a key component for any action recognition system that claims to be truly real-time. In this paper we present a conceptually simple and computationally efficient method for real-time hu...
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Real-time feature extraction is a key component for any action recognition system that claims to be truly real-time. In this paper we present a conceptually simple and computationally efficient method for real-time human activity recognition based on simple statistical features. Such features are very cheap to compute and form a relatively low dimensional feature space in which classification can be carried out robustly. On the Weizmann dataset, the proposed method achieves encouraging recognition results with an average rate up to 97.8%. These results are in a good agreement with the literature. Further, the method achieves real-time performance, and thus can offer timing guarantees to real-time applications.
This paper presents a method to improve the search rate of Max-Min Ant System for the traveling salesman problem. The proposed method gives deviations from the initial pheromone trails by using a set of local optimal ...
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This paper presents a method to improve the search rate of Max-Min Ant System for the traveling salesman problem. The proposed method gives deviations from the initial pheromone trails by using a set of local optimal solutions calculated in advance. Max-Min Ant System has demonstrated impressive performance, but the rate of search is relatively low. Considering the generic purpose of stochastic search algorithms, which is to find near optimal solutions subject to time constraints, the rate of search is important as well as the quality of the solution. The experimental results using benchmark problems with 51 to 318 cities suggested that the proposed method is better than the conventional method in both the quality of the solution and the rate of search.
The paper considers automatic visual recognition of signed expressions. The proposed method is based on modeling gestures with subunits, which is similar to modeling speech by means of phonemes. To define the subunits...
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The paper considers automatic visual recognition of signed expressions. The proposed method is based on modeling gestures with subunits, which is similar to modeling speech by means of phonemes. To define the subunits a data-driven procedure is applied. The procedure consists in partitioning time series, extracted from video, into subsequences which form homogeneous groups. The cut points are determined by an immune optimization procedure based on quality assessment of the resulting clusters. In the paper the problem is formulated, its solution method is proposed and experimentally verified on a database of 100 Polish words. The results show that our subunit-based classifier outperforms its whole-word-based counterpart, which is particularly evident when new words are recognized on the basis of a small number of examples.
This paper presents an iris recognition method based on the two dimensional dual-tree complex wavelet transform (2D-CWT) and the support vector machines (SVM). 2D-CWT has such significant properties as the approximate...
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This paper presents an iris recognition method based on the two dimensional dual-tree complex wavelet transform (2D-CWT) and the support vector machines (SVM). 2D-CWT has such significant properties as the approximate shift-invariance, high directional selectivity and computationally much more efficient. These properties are very useful in invariant iris recognition. SVM is used as a classifier and several kernel functions are tested in the experiments. The obtained experimental results showed that the proposed approach enhanced the classification accuracy. The experimental results were also compared with the k-NN and Naïve Bayes classifiers to demonstrate the efficacy of the proposed technique.
Sales on the Internet have increased significantly during the last decade, and so, it is crucial for companies to retain customers on their web site. Among all strategies towards this goal, providing customers with a ...
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Sales on the Internet have increased significantly during the last decade, and so, it is crucial for companies to retain customers on their web site. Among all strategies towards this goal, providing customers with a flexible search tool is a crucial issue. In this paper, we propose an approach, called TIGER, for handling such flexibility automatically. More precisely, if the search criteria of a given query to a relational table or a Web catalog are too restrictive, our approach computes a new query combining extensions of the criteria. This new query maximizes the quality of the answer, while being as close as possible to the original query. Experiments show that our approach improves the quality of queries, in the sense explained just above.
There are numerous application of DNA microar-rays. The most frequently used application is the one used to research gene expression. The aim is, among others, to detect symptoms of illnesses in tissues, to predict th...
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There are numerous application of DNA microar-rays. The most frequently used application is the one used to research gene expression. The aim is, among others, to detect symptoms of illnesses in tissues, to predict the predisposition for some illnesses and for personal identification. The huge amount of information obtained from DNA microarrays in range of tens of thousands of genes causes many difficulties. Many algorithms, for instance, many rough set methods cannot be applied for this reason and due to low number of training objects tend to overfit. In this paper, we aim at presenting gene separation and classification methods by means of the granular classifier based on weighted voting, which was investigated recently by Polkowski and Artiemjew. Results of the research show that the obtained results are in many cases better than some standard methods such as rough set standard exhaustive classifier and k-nearest neighbor.
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