Nowadays computer scientists are faced with fast growing and permanently evolving data, which are represented as observations made sequentially in time. A common problem in the data mining community is the recognition...
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this paper presents three disease diagnosis systems using patternrecognition based on genetic algorithm and neural networks. All systems deal with feature selection and classification. Genetic algorithm chooses subse...
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ISBN:
(纸本)9781618040282
this paper presents three disease diagnosis systems using patternrecognition based on genetic algorithm and neural networks. All systems deal with feature selection and classification. Genetic algorithm chooses subsets of features for the input of the classifier (neural network) and the accuracy of the classifier determine the percentage of effectiveness of each subsets of features. the classifiers using in this paper are general regression neural network (GRNN), radial basis function (RBF) and radial basis network exact fit (RBEF). We use breast cancer and hepatitis disease datasets taken from UCI machinelearningdatabase as medical dataset. the system performances are estimated by classification accuracy and they are compared with similar methods without feature selection.
A method for simultaneous non-Gaussian data clustering, feature selection and outliers rejection is proposed in this paper. the proposed approach is based on finite generalized Dirichlet mixture models learned within ...
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ISBN:
(纸本)9783642217869
A method for simultaneous non-Gaussian data clustering, feature selection and outliers rejection is proposed in this paper. the proposed approach is based on finite generalized Dirichlet mixture models learned within a framework including expectation-maximization updates for model parameters estimation and minimum message length criterion for model selection. through a challenging application involving texture images discrimination, it is demonstrated that the developed procedure performs effectively in avoiding outliers and selecting relevant features.
the aim of this talk is to explain a pioneering exploratory research endeavour that attempts to merge two completely different fields in Computer Science so as to yield very fascinating results. these are the well-est...
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ISBN:
(纸本)9783642217869
the aim of this talk is to explain a pioneering exploratory research endeavour that attempts to merge two completely different fields in Computer Science so as to yield very fascinating results. these are the well-established fields of Neural Networks (NNs) and Adaptive data Structures (ADS) respectively. the field of NNs deals withthe training and learning capabilities of a large number of neurons, each possessing minimal computational properties. On the other hand, the field of ADS concerns designing, implementing and analyzing data structures which adaptively change with time so as to optimize some access criteria. In this talk, we shall demonstrate how these fields can be merged, so that the neural elements are themselves linked together using a data structure. this structure can be a singly-linked or doubly-linked list, or even a Binary Search Tree (BST). While the results themselves are quite generic, in particular, we shall, as a prima facie case, present the results in which a Self-Organizing Map (SOM) with an underlying BST structure can be adaptively re-structured using conditional rotations. these rotations on the nodes of the tree are local and are performed in constant time, guaranteeing a decrease in the Weighted Path Length of the entire tree. As a result, the algorithm, referred to as the Tree-based Topology-Oriented SOM with Conditional Rotations (TTO-CONROT), converges in such a manner that the neurons are ultimately placed in the input space so as to represent its stochastic distribution. Besides, the neighborhood properties of the neurons suit the best BST that represents the data.
A disjunctive model of box bicluster and tricluster analysis is considered. A least-squares locally-optimal one cluster method is proposed, oriented towards the analysis of binary data. the method involves a parameter...
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ISBN:
(纸本)9783642218804
A disjunctive model of box bicluster and tricluster analysis is considered. A least-squares locally-optimal one cluster method is proposed, oriented towards the analysis of binary data. the method involves a parameter, the scale shift, and is proven to lead to "contrast" box bi- and tri-clusters. An experimental study of the method is reported.
Support Vector machine (Support Vector machine, SVM) demonstrates many unique advantages in solving the small sample, nonlinear and high dimensional patternrecognition, and can promote to the application of the use o...
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Affective computing (AC) is a unique discipline which includes modeling affect using one or multiple modalities by drawing on techniques from many different fields. AC often deals with problems that are known to be ve...
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ISBN:
(纸本)9783642245701
Affective computing (AC) is a unique discipline which includes modeling affect using one or multiple modalities by drawing on techniques from many different fields. AC often deals with problems that are known to be very complex and multi-dimensional, involving different kinds of data (numeric, symbolic, visual etc.). However, withthe advancement of machinelearning techniques, a lot of those problems are now becoming more tractable.
In this paper, we propose a nearest neighbor based outlier detection algorithm, N DoT. We introduce a parameter termed as Nearest Neighbor Factor (NNF) to measure the degree of outlierness of a point with respect to i...
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ISBN:
(纸本)9783642217869
In this paper, we propose a nearest neighbor based outlier detection algorithm, N DoT. We introduce a parameter termed as Nearest Neighbor Factor (NNF) to measure the degree of outlierness of a point with respect to its neighborhood. Unlike the previous outlier detection methods N DoT works by a voting mechanism. Voting mechanism binarizes the decision compared to the top-N style of algorithms. We evaluate our method experimentally and compare results of N DoT with a classical outlier detection method LOF and a recently proposed method LDOF. Experimental results demonstrate that N DoT outperforms LDOF and is comparable with LOF.
the proposition of adaptive selection of rule quality measures during rules induction is presented in the paper. In the applied algorithm the measures decide about a form of elementary conditions in a rule premise and...
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ISBN:
(纸本)9783642218804
the proposition of adaptive selection of rule quality measures during rules induction is presented in the paper. In the applied algorithm the measures decide about a form of elementary conditions in a rule premise and monitor a pruning process. An influence of filtration algorithms on classification accuracy and a number of obtained rules is also presented. the analysis has been done on twenty one benchmark data sets.
Semantic network is an information model of knowledge domain. Objects and their relations are specified with an attributed graph. Multistripe layout is suitable for visualization of relations incident to the selected ...
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ISBN:
(纸本)9783642218804
Semantic network is an information model of knowledge domain. Objects and their relations are specified with an attributed graph. Multistripe layout is suitable for visualization of relations incident to the selected set of objects. the method provides a compact drawing that is guaranteed to avoid link crossings and label overlaps for objects and relations of corresponding subnetwork. In this paper we describe a common scheme of the multistripe layout approach and propose the way of visualization of semantic network fragments. these fragments may contain additional relations and objects in comparison with subnetworks considered earlier.
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