James F. Peters

Transactions on rough sets XVII

Three-Valued Logics, Uncertainty Management and Rough Sets.- Standard Errors of Indices in Rough Set Data Analysis.- Proximity System: A Description-Based System for Quantifying the Nearness or Apartness of Visual Rough Sets.- Rough Sets and Matroids.- An Efficient Approach for Fuzzy Decision Reduct Computation.- Rough Sets in Economy and Finance.- Algorithms for Similarity Relation Learning from High Dimensional Data.

Zdzisław pawlak commemorating his life and work

Zdzislaw Pawlak will be remembered as a great human being with exceptional humility, wit and kindness as well as an extraordinarily innovative researcher with exceptional stature. His research contributions have had far-reaching implications inasmuch as his works are fundamental in establishing new perspectives for scientific research in a wide spectrum of fields.

Professor Zdzisław Pawlak (1926-2006): Founder of the Polish School of Artificial Intelligence

This chapter is dedicated to the memory of Professor Zdzislaw Pawlak, founder of the Polish school of Artificial Intelligence and one of the pioneers in Computer Engineering and Computer Science with worldwide influence.

Transactions on Rough Sets XX

Feature selecting is considered as one of the most important pre-process methods in machine learning, data mining and bioinformatics. By applying pre-process techniques, we can defy the curse of dimensionality by reducing computational and storage costs, facilitate data understanding and visualization, and diminish training and testing times, leading to overall performance improvement, especially when dealing with large datasets. Correlation feature selection method uses a conventional merit to evaluate different feature subsets. In this paper, we propose a new merit by adapting and employing of correlation feature selection in conjunction with fuzzy-rough feature selection, to improve the effectiveness and quality of the conventional methods. It also outperforms the newly introduced gradient boosted feature selection, by selecting more relevant and less redundant features. The two-step experimental results show the applicability and efficiency of our proposed method over some well known and mostly used datasets, as well as newly introduced ones, especially from the UCI collection with various sizes from small to large numbers of features and samples.

Transactions on Rough Sets XIX

A Uniform Framework for Rough Approximations Based on Generalized Quantifiers.- PRE and Variable Precision Models in Rough Set Data Analysis.- Three Approaches to Deal with Tests for Inconsistent Decision Tables - Comparative Study.- Searching for Reductive Attributes in Decision Tables.- Sequential Optimization of c-Decision Rules Relative to Length, Coverage and Number of Misclassifications.- Toward Qualitative Assessment of Rough Sets in Terms of Decision Attribute Values in Simple Decision Systems over Ontological Graphs.- Predicting the Presence of Serious Coronary Artery Disease Based on 24 Hour Holter ECG Monitoring.- Interface of Rough Set Systems and Modal Logics: A Survey.- A Semantic Text Retrieval for Indonesian Using Tolerance Rough Sets Models.- Some Transportation Problems Under Uncertain Environments.

Transactions on Rough Sets XVIII

A rough intuitionistic fuzzy set is the result of approximation of an intuitionistic fuzzy set with respect to a crisp approximation space. In this paper, we investigate topological structures of rough intuitionistic fuzzy sets. We first show that a reflexive crisp rough approximation space can induce an intuitionistic fuzzy Alexandrov space. It is proved that the lower and upper rough intuitionistic fuzzy approximation operators are, respectively, an intuitionistic fuzzy interior operator and an intuitionistic fuzzy closure operator if and only if the binary relation in the crisp approximation space is reflexive and transitive. We then verify that a similarity crisp approximation space can produce an intuitionistic fuzzy clopen topological space. We further examine sufficient and necessary conditions that an intuitionistic fuzzy interior (closure, respectively) operator derived from an intuitionistic fuzzy topological space can associate with a reflexive and transitive crisp relation such that the induced lower (upper, respectively) rough intuitionistic fuzzy approximation operator is exactly the intuitionistic fuzzy interior (closure, respectively) operator.