Papers by Hung Son Nguyen
CS&P, 2019
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Springer eBooks, 2013
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Studies in computational intelligence, 2013
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Cognitive technologies, 2004
Searching for a binary partition of attribute domains is an important task in data mining. It is ... more Searching for a binary partition of attribute domains is an important task in data mining. It is present in both decision tree construction and discretization. The most important advantages of decision tree methods are compactness and clearness of knowledge representation as well as high accuracy of classification. Decision tree algorithms also have some drawbacks. In cases of large data tables, existing decision tree induction methods are often inefficient in both computation and description aspects. Another disadvantage of standard decision tree methods is their instability, i e, small data deviations may require a significant reconstruction of the decision tree. We present novelsoft discretizationmethods usingsoft cutsinstead of traditionalcrisp(or sharp) cuts. This new concept makes it possible to generate more compact and stable decision trees with high accuracy of classification. We also present an efficient method for soft cut generation from large databases.
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Electronic Notes in Theoretical Computer Science, Mar 1, 2003
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Ercim News, 2019
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Web Intelligence/IAT Workshops, 2013
Tolerance Rough Set Model (TRSM) has been introduced as a tool for approximation of hidden concep... more Tolerance Rough Set Model (TRSM) has been introduced as a tool for approximation of hidden concepts in text databases. In recent years, numerous successful applications of TRSM in web intelligence including text classification, clustering, thesaurus generation, semantic indexing, and semantic search, etc., have been proposed. This paper will review the fundamental concepts of TRSM, some of its possible extensions and some typical applications of TRSM in text mining. Moreover, the architecture o a semantic information retrieval system, called SONCA, will be presented to demonstrate the main idea as well as stimulate the further research on TRSM.
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Lecture Notes in Computer Science, 2022
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Page 1. A method of web search result clustering based on rough sets Chi Lang Ngo, Hung Son Nguye... more Page 1. A method of web search result clustering based on rough sets Chi Lang Ngo, Hung Son Nguyen (∗) Institute of Mathematics, Warsaw University Banacha 2, 02-097 Warsaw, Poland (∗) E-mail (contact person): son@mimuw.edu.pl Abstract ...
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IGI Global eBooks, May 24, 2011
This chapter presents the Boolean reasoning approach to problem solving and its applications in R... more This chapter presents the Boolean reasoning approach to problem solving and its applications in Rough sets. The Boolean reasoning approach has become a powerful tool for designing effective and accurate solutions for many problems in decision-making, approximate reasoning and optimization. In recent years, Boolean reasoning has become a recognized technique for developing many interesting concept approximation methods in rough set theory. This chapter presents a general framework for concept approximation by combining the classical Boolean reasoning method with many modern techniques in machine learning and data mining. This modified approach - called “the approximate Boolean reasoning” methodology - has been proposed as an even more powerful tool for problem solving in rough set theory and its applications in data mining. Through some most representative applications in many KDD problems including feature selection, feature extraction, data preprocessing, classification of decision rules and decision trees, association analysis, the author hopes to convince that the proposed approach not only maintains all the merits of its antecedent but also owns the possibility of balancing between quality of the designed solution and its computational time.
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Springer eBooks, 1998
We study the relationship between reduct problem in Rough Sets theory and the problem of real val... more We study the relationship between reduct problem in Rough Sets theory and the problem of real value attribute discretization. We consider the problem of searching for a minimal set of cuts on attribute domains that preserves discernibility of objects with respect to any ...
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Springer eBooks, Nov 1, 2006
Invited Papers.- Decision Trees and Flow Graphs.- Granular Computing - The Concept of Generalized... more Invited Papers.- Decision Trees and Flow Graphs.- Granular Computing - The Concept of Generalized Constraint-Based Computation.- Bipolar Representations in Reasoning, Knowledge Extraction and Decision Processes.- Kansei Engineering and Rough Sets Model.- Stochastic Approach to Rough Set Theory.- Commemorative Papers for Professor Pawlak.- Zdzis?aw Pawlak Commemorating His Life and Work.- Pawlak Rough Set Model, Medical Reasoning and Rule Mining.- Logics in Rough Sets.- Algebras of Terms in Pawlak's Information Systems.- Monads Can Be Rough.- On Testing Membership to Maximal Consistent Extensions of Information Systems.- The Research of Rough Sets in Normed Linear Space.- Two Kinds of Rough Algebras and Brouwer-Zadeh Lattices.- Logics in Fuzzy Sets.- Balanced Fuzzy Gates.- Triangle Algebras: Towards an Axiomatization of Interval-Valued Residuated Lattices.- Fuzzy-Rough Hybridization.- An Approach to Parameterized Approximation of Crisp and Fuzzy Sets.- Rough Fuzzy Set Approximations in Fuzzy Formal Contexts.- Webpage Classification with ACO-Enhanced Fuzzy-Rough Feature Selection.- Approximate and Uncertain Reasoning.- Association Reducts: Complexity and Heuristics.- Planning Based on Reasoning About Information Changes.- Rough Approximation Operators in Covering Approximation Spaces.- Variable Precision Rough Set Models.- A New Method for Discretization of Continuous Attributes Based on VPRS.- On Variable Consistency Dominance-Based Rough Set Approaches.- Variable-Precision Dominance-Based Rough Set Approach.- Incomplete/Nondeterministic Information Systems.- Applying Rough Sets to Data Tables Containing Imprecise Information Under Probabilistic Interpretation.- Ensembles of Decision Rules for Solving Binary Classification Problems in the Presence of Missing Values.- Expanding Tolerance RST Models Based on Cores of Maximal Compatible Blocks.- Local and Global Approximations for Incomplete Data.- Missing Template Decomposition Method and Its Implementation in Rough Set Exploration System.- On Possible Rules and Apriori Algorithm in Non-deterministic Information Systems.- Decision Support.- Generalized Conflict and Resolution Model with Approximation Spaces.- Rough Set Approach to Customer Satisfaction Analysis.- Utility Function Induced by Fuzzy Target in Probabilistic Decision Making.- Multi-criteria Decision Support.- Dominance-Based Rough Set Approach to Decision Involving Multiple Decision Makers.- Quality of Rough Approximation in Multi-criteria Classification Problems.- Rough-Set Multiple-Criteria ABC Analysis.- Rough Sets in KDD.- A Method of Generating Decision Rules in Object-Oriented Rough Set Models.- Knowledge Reduction in Set-Valued Decision Information System.- Local Reducts and Jumping Emerging Patterns in Relational Databases.- Mining Rough Association from Text Documents.- NetTRS - Induction and Postprocessing of Decision Rules.- Outlier Detection Based on Rough Membership Function.- Rough Sets in Medicine.- An Approach to a Rough Set Based Disease Inference Engine for ECG Classification.- Attribute Selection for EEG Signal Classification Using Rough Sets and Neural Networks.- Automatic Planning of Treatment of Infants with Respiratory Failure Through Rough Set Modeling.- Developing a Decision Model for Asthma Exacerbations: Combining Rough Sets and Expert-Driven Selection of Clinical Attributes.- Granular Computing.- A GrC-Based Approach to Social Network Data Protection.- An Interpretation of Flow Graphs by Granular Computing.- Attribute Reduction Based on Granular Computing.- Methodological Identification of Information Granules-Based Fuzzy Systems by Means of Genetic Optimization.- Optimization of Information Granulation-Oriented Fuzzy Set Model Using Hierarchical Fair Competition-Based Parallel Genetic Algorithms.- Grey Systems.- A Grey-Based Rough Set Approach to Suppliers Selection Problem.- A Hybrid Grey-Based Dynamic Model for International Airlines Amount Increase Prediction.- On the Combination of Rough Set Theory and Grey Theory Based on Grey Lattice Operations.- Ontology and Mereology.- An Ontology-Based First-Order Modal Logic.- Enhancing a Biological Concept Ontology to Fuzzy Relational Ontology with Relations Mined from Text.- On a Parthood Specification Method for Component Software.- Ontology Driven Concept Approximation.- Statistical Mathods.- A Statistical Method for Determining Importance of Variables in an Information System.- Distribution of Determinants of Contingency Matrix.- Interpretation of Contingency Matrix Using Marginal Distributions.- Machine Learning.- A Model of Machine Learning Based on User Preference of Attributes.- Combining Bi-gram of Character and Word to Classify Two-Class Chinese Texts in Two Steps.- Combining Monte Carlo Filters with Support Vector Machines for Option Price Forecasting.- Domain Knowledge Assimilation by Learning Complex Concepts.- Learning Compound Decision Functions for Sequential Data in Dialog with Experts.- Sampling of Virtual…
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... Our special thanks go to Tsuneo Okura, Mika Kuroda, Daisuke Toyama, Namiko Sugimoto, and Masa... more ... Our special thanks go to Tsuneo Okura, Mika Kuroda, Daisuke Toyama, Namiko Sugimoto, and Masahiro Kagawa for their help ... JunSun, WenboXu, WeiFang Identification and Speed Control of Ultrasonic Motors Based on Modified Immune Algorithm and Elman Neural Networks ...
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Springer eBooks, 2008
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Springer eBooks, 2023
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2022 IEEE International Conference on Big Data (Big Data)
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ERCIM News, 2016
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Searching for a binary partition of attribute domains is an i mportant task in data mining. It is... more Searching for a binary partition of attribute domains is an i mportant task in data mining. It is present in both decision tree construction and discretization. The most important advantages of decision tree methods are compactness and cle rness of knowledge representation as well as high accuracy of classification. Decision t ree algorithms also have some drawbacks. In cases of large data tables, existing decision tree induction methods are often inefficient in both computation and description aspects. An other disadvantage of standard decision tree methods is their instability, i.e., small dat a deviations may require a significant reconstruction of the decision tree. We present novel soft discretizationmethods usingsoft cuts instead of traditional crisp (or sharp) cuts. This new concept makes it possible to generate more compact and stable decision trees with high accur y of classification. We also present an efficient method for soft cut generation from larg e databases.
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In recent years many machine learning concepts and methods were developed on the set of pairs of ... more In recent years many machine learning concepts and methods were developed on the set of pairs of objects. In this paper, the set of all pairs of objects is called the pairwise space. Let us notice that if the set of objects X = {x1,x2, . . . ,xn} consists of n instances, then the pairwise space contains O(n2) pairs. Thus why the straightforward implementations of those methods are not applicable for big data sets with millions of objects. The main concepts in rough set theory (RS) such as reducts, lower and upper approximations, decision rules or discretizations have been defined in term of the discernibility matrix, which is a form of the pairwise space [1]. For example, in minimal decision reduct problem, we are looking for the minimal subset of features that preserves the discernibility between objects from different decision classes [2]. Support Vector Machine (SVM) is also a classification method described as an optimization problem over the pairwise space [3]. The initial idea...
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Papers by Hung Son Nguyen