Browsing Advanced Reasoning Group by Title

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Browsing Advanced Reasoning Group by Title

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  • Fuzzy-Rough Feature Significance for Fuzzy Decision Trees 
    Jensen, Richard; Shen, Qiang (2005)
    Crisp decision trees are one of the most popular classification algorithms in current use within data mining and machine learning. However, although they possess many desirable features, they lack the ability to model ...
  • Fuzzy-rough instance selection 
    Jensen, Richard; Cornelis, Chris (2010)
    Rough set theory provides a useful mathematical foundation for developing automated computational systems that can help understand and make use of imperfect knowledge. Since its introduction, this theory has been successfully ...
  • Fuzzy-Rough Nearest Neighbour Classification 
    Jensen, Richard; Cornelis, Chris (2011-12-31)
  • Fuzzy-Rough Nearest Neighbour Classification and Prediction 
    Cornelis, Chris; Jensen, Richard (2011-09-07)
    Nearest neighbour (NN) approaches are inspired by the way humans make decisions, comparing a test object to previously encountered samples. In this paper, we propose an NN algorithm that uses the lower and upper approximations ...
  • Fuzzy Rough Positive Region based Nearest Neighbour Classification 
    Verbiest, Nele; Cornelis, Chris; Jensen, Richard (IEEE, 2012)
    This paper proposes a classifier that uses fuzzy rough set theory to improve the Fuzzy Nearest Neighbour (FNN) classifier. We show that previous attempts to use fuzzy rough set theory to improve the FNN algorithm have some ...
  • Fuzzy rough set as feature selection for QSAR modeling of 2,4,5-trisubstituted imidazoles, nontoxic modulators of P-glycoprotein mediated multidrug resistance 
    Vander Heyden, Yvan; Dejaegher, Bieke; Jensen, Richard; Funar-Timofei, Simona; Goodarzi, Mohammad (2011-07-13)
    In cancer chemotherapy, multidrug resistance (MDR) is a major clinical problem which occurs by an influential mechanism and which leads to the failure of cancer chemotherapy and/or a relapse of the cancer. In this study, ...
  • Fuzzy-Rough Sets Assisted Attribute Selection 
    Shen, Qiang; Jensen, Richard (2007)
    Attribute selection (AS) refers to the problem of selecting those input attributes or features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition ...
  • Fuzzy rules from ant-inspired computation 
    Shen, Qiang; Galea, Michelle (2004)
    A new approach to fuzzy rule induction from historical data is presented. The implemented system - FRANTIC - is a tested on a simple classification problem against a fuzzy tree induction algorithm, a genetic algorithm, and ...
  • Fuzzy Sets and Rough Sets for Scenario Modelling and Analysis 
    Shen, Qiang (2009)
    Both fuzzy set theory and rough set theory play an important role in data-driven, systems modelling and analysis. They have been successfully applied to building various intelligent decision support systems (amongst many ...
  • A fuzzy-XCS classifier system with linguistic hedges 
    Marin-Blázquez, Javier; Shen, Qiang (2008)
    Many real-world problems require the development and application of algorithms that automatically generate human interpretable knowledge from historical data. Most existing algorithms for rule induction from imprecise data ...
  • Generalisation of Scale and Move Transformation-Based Fuzzy Interpolation 
    Shen, Qiang; Longzhi, Yang (2011)
    Fuzzy interpolative reasoning has been extensively studied due to its ability to enhance the robustness of fuzzy systems and reduce system complexity. In particular, the scale and move transformation-based approach is able ...
  • Granularity and disaggregation in compositional modelling with applications to ecological systems 
    Keppens, Jeroen; Shen, Qiang (2006-12)
    In the past decade, compositional modelling (CM) has established itself as the predominant knowledge-based approach to construct mathematical (simulation) models automatically. Although it is mainly applied to physical ...
  • Hybrid Fuzzy-Rough Rule Induction and Feature Selection 
    Cornelis, Chris; Jensen, Richard; Shen, Qiang (2009)
    The automated generation of feature pattern-based if-then rules is essential to the success of many intelligent pattern classifiers, especially when their inference results are expected to be directly human-comprehensible. ...
  • Identifying Novice Difficulties in Object Orientated Design 
    Thomasson, Benjy; Thomas, Lynda; Ratcliffe, Mark (2006)
    We report on a study of novice programmers’ object oriented class designs. These designs were analysed to discover what faults could be discovered. The two most common faults related to non-referenced classes (inability ...
  • Intelligent Hybrid Approach to False Identity Detection 
    Boongoen, Tossapon; Shen, Qiang (2009)
    Combating identity fraud is prominent and urgent since false identity has become the common denominator of all serious crime. Among many identified identity attributes, personal names are commonly falsified or aliased by ...
  • Intelligent systems for decision support 
    Shen, Qiang (2009)
    Decision support systems play an important role in many application domains. For instance, in the detection of serious crime, including terrorist activity, an intelligent system which is capable of automated modelling and ...
  • Interval-valued Fuzzy-Rough Feature Selection and Application for Handling Missing Values in Datasets 
    Shen, Qiang; Jensen, Richard (2008)
    One of the many successful applications of rough set theory has been to the area of feature selection. The rough set ideology of using only the supplied data and no other information has many benefits, where most other ...
  • Interval-valued Fuzzy-Rough Feature Selection in Datasets with Missing Values 
    Jensen, Richard; Shen, Qiang (2009)
    One of the many successful applications of rough set theory has been to the area of feature selection. The rough set principle of using only the supplied data and no other information has many benefits, where most other ...
  • Iterative vs Simultaneous Fuzzy Rule Induction 
    Galea, Michelle; Shen, Qiang (2005)
    Iterative rule learning is a common strategy for fuzzy rule induction using stochastic population-based algorithms (SPBAs) such as Ant Colony Optimisation (ACO) and genetic algorithms. Several SPBAs are run in succession ...
  • Knowledge representation for fuzzy model composition 
    Fu, Xin; Shen, Qiang (2007-06)
    Compositional Modelling (CM) has been applied to synthesize automatically plausible scenarios in many problem domains with promising results. However, due to the lack of capability to deal with imprecise or illdefined ...

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