A novel Genetic Programming (GP) paradigm called Co-evolutionary Rule-Chaining Genetic Programming (CRGP) has been proposed to learn the relationships among attributes represented by a set of classification rules for ...
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In this paper, we propose an intelligent grading system using heterogeneous linguistic resources. We used latent semantic kernel as one resource in former research and found that a deficit of indexed terms gave rise t...
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A common objective in image analysis is dimensionality reduction. the most often used data-exploratory technique withthis objective is principal component analysis. We propose a new method based on the projection of ...
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ISBN:
(纸本)3540228810
A common objective in image analysis is dimensionality reduction. the most often used data-exploratory technique withthis objective is principal component analysis. We propose a new method based on the projection of the images as matrices after a Procrustes rotation and show that it leads to a better reconstruction of images.
Applications acquiring data from multiple sensors have to properly refer data to observables. On-line classification and clustering as basic tools for performing information fusion are computationally viable. However,...
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ISBN:
(纸本)9783319248349;9783319248332
Applications acquiring data from multiple sensors have to properly refer data to observables. On-line classification and clustering as basic tools for performing information fusion are computationally viable. However, they poorly exploit temporal relationships in data as patterns mining methods can do. Hence, this paper introduces a new algorithm for the correlations mining in the proposed graph-stream data structure. It can iteratively find relationships in complex data, even if they are partially unsynchronized or disturbed. Retrieved patterns (traces) can be used directly to fuse multi-perspective observations. the algorithm's evaluation was conducted during experiments on artificial data sets while its computational efficiency and results quality were measured.
In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Furt...
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the plots of some reputation time series superficially resemble plots of white noise. this raises the question of whether or not the analysis of sentiment to produce a reputation index actually generates nothing more ...
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ISBN:
(纸本)9783319689357;9783319689340
the plots of some reputation time series superficially resemble plots of white noise. this raises the question of whether or not the analysis of sentiment to produce a reputation index actually generates nothing more than noise. the question is answered by using the Box-Ljung statistical test to establish that the reputation time series considered in this analysis cannot be viewed as white noise. this result is supported by applying a new test based on cross-correlations of reputation time series with white noise time series.
We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [1]. But whereas the GTM i...
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ISBN:
(纸本)9783540772255
We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [1]. But whereas the GTM is an extension of a mixture of experts, this model is an extension of a product of experts [6]. We show visualisation and clustering results on a data set composed of video data of lips uttering 5 Korean vowels and show that the new mapping achieves better results than the standard Self-Organizing Map.
Half of the general population experiences a headache during any given year. Medical data and information in turn provide knowledge on which physicians base their decisions and actions but, in general, it is not easy ...
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ISBN:
(纸本)9783030034962;9783030034955
Half of the general population experiences a headache during any given year. Medical data and information in turn provide knowledge on which physicians base their decisions and actions but, in general, it is not easy to manage them. It becomes increasingly necessary to extract useful knowledge and make scientific decisions for diagnosis and treatment of this disease from the database. this paper presents comparison of data and attribute selected features by automatic machine learning methods and algorithms, and by diagnostic tools and expert physicians, almost all from the last decade.
the difficulty of the many classification tasks lies in the analyzed data nature, as disproportionate number of examples from different class in a learning set. Ignoring this characteristics causes that canonical clas...
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ISBN:
(纸本)9783030034962;9783030034955
the difficulty of the many classification tasks lies in the analyzed data nature, as disproportionate number of examples from different class in a learning set. Ignoring this characteristics causes that canonical classifiers display strongly biased performance on imbalanced datasets. In this work a novel classifier ensemble forming technique for imbalanced datasets is presented. On the one hand it takes into consideration selected features used for training individual classifiers, on the other hand it ensures an appropriate diversity of a classifier ensemble. the proposed method was tested on the basis of the computer experiments carried out on the several benchmark datasets. their results seem to confirm the usefulness of the proposed concept.
Evolutionary instance selection outperforms in most cases non-evolutionary methods, also for function approximation tasks considered in this work. However, as the number of instances encoded into the chromosome grows,...
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ISBN:
(纸本)9783030034931;9783030034924
Evolutionary instance selection outperforms in most cases non-evolutionary methods, also for function approximation tasks considered in this work. However, as the number of instances encoded into the chromosome grows, finding the optimal subset becomes more difficult, especially that running the optimization too long leads to over-fitting. A solution to that problem, which we evaluate in this work is to reduce the search space by clustering the dataset, run the instance selection algorithm for each cluster and combine the results. We also address the issue of properly processing the instances close to the cluster boundaries, as this is where the drop of accuracy can appear. the method is experimentally verified on several regression datasets withthousands of instances.
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