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Improving feature selection process resistance to failures caused by curse-of-dimensionality effects

Petr Somol, Jiří Grim, Jana Novovičová, Pavel Pudil (2011)

Kybernetika

The purpose of feature selection in machine learning is at least two-fold - saving measurement acquisition costs and reducing the negative effects of the curse of dimensionality with the aim to improve the accuracy of the models and the classification rate of classifiers with respect to previously unknown data. Yet it has been shown recently that the process of feature selection itself can be negatively affected by the very same curse of dimensionality - feature selection methods may easily over-fit...

Innovative applications of associative morphological memories for image processing and pattern recognition.

Manuel Graña, Peter Sussner, Gerhard Ritter (2003)

Mathware and Soft Computing

Morphological Associative Memories have been proposed for some image denoising applications. They can be applied to other less restricted domains, like image retrieval and hyperspectral image unsupervised segmentation. In this paper we present these applications. In both cases the key idea is that Autoassociative Morphological Memories selective sensitivity to erosive and dilative noise can be applied to detect the morphological independence between patterns. Linear unmixing based on the sets of...

Integrated region-based segmentation using color components and texture features with prior shape knowledge

Mehryar Emambakhsh, Hossein Ebrahimnezhad, Mohammad Hossein Sedaaghi (2010)

International Journal of Applied Mathematics and Computer Science

Segmentation is the art of partitioning an image into different regions where each one has some degree of uniformity in its feature space. A number of methods have been proposed and blind segmentation is one of them. It uses intrinsic image features, such as pixel intensity, color components and texture. However, some virtues, like poor contrast, noise and occlusion, can weaken the procedure. To overcome them, prior knowledge of the object of interest has to be incorporated in a top-down procedure...

Interpretable random forest model for identification of edge 3-uncolorable cubic graphs

Adam Dudáš, Bianka Modrovičová (2023)

Kybernetika

Random forest is an ensemble method of machine learning that reaches a high level of accuracy in decision-making but is difficult to understand from the point of view of interpreting local or global decisions. In the article, we use this method as a means to analyze the edge 3-colorability of cubic graphs and to find the properties of the graphs that affect it most strongly. The main contributions of the presented research are four original datasets suitable for machine learning methods, a random...

Interpretation and optimization of the k -means algorithm

Kristian Sabo, Rudolf Scitovski (2014)

Applications of Mathematics

The paper gives a new interpretation and a possible optimization of the well-known k -means algorithm for searching for a locally optimal partition of the set 𝒜 = { a i ∈ ℝ n : i = 1 , ⋯ , m } which consists of k disjoint nonempty subsets π 1 , ⋯ , π k , 1 ≤ k ≤ m . For this purpose, a new divided k -means algorithm was constructed as a limit case of the known smoothed k -means algorithm. It is shown that the algorithm constructed in this way coincides with the k -means algorithm if during the iterative procedure no data points appear in the Voronoi diagram....

Interpretation of pattern classification results, obtained from a test set

Edgard Nyssen (1998)

Kybernetika

The present paper presents and discusses a methodology for interpreting the results, obtained from the application of a pattern classifier to an independent test set. It addresses the problem of testing the random classification null hypothesis in the multiclass case, by introducing an exact probability technique. The discussion of this technique includes the presentation of an interval estimation technique for the probability of correct classification, which is slightly more accurate than the ones...

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