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Now showing items 1-19 of 19

  • Adaptive Offset Subspace Self-Organizing Map: An Application to Handwritten Digit Recognition 

    Zheng, Huicheng; Cunningham, Padraig; Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2006-06-23)
    An Adaptive-Subspace Self-Organizing Map (ASSOM) can learn a set of ordered linear subspaces which correspond to invariant classes. However the basic ASSOMcannot properly learn linear manifolds that are shifted away ...
  • A Case-Based Technique for Tracking Concept Drift in Spam Filtering 

    Delany, Sarah Jane; Cunningham, Padraig; Tsymbal, Alexey; Coyle, Lorcan (Trinity College Dublin, Department of Computer Science, 2004-08-17)
    Clearly, machine learning techiques can play an important role in filtering spam email because ample training data is available to build a robust classifier. However, spam filtering is a particularly challenging task as ...
  • Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction 

    Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2006)
    Inductive learning systems have been successfully applied in a number of medical domains. It is generally accepted that the highest accuracy results that an inductive learning system can achieve depend on the quality ...
  • A Comparison of Ensemble and Case-Base Maintenance Techniques for Handling Concept Drift in Spam Filtering 

    Delany, Sarah Jane; Cunningham, Padraig; Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2005)
    The problem of concept drift has recently received considerable attention in machine learning research. One important practical problem where concept drift needs to be addressed is spam filtering. The literature on ...
  • Diversity in Ensemble Feature Selection 

    Tsymbal, Alexey; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2003)
    Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown ...
  • Does Relevance Matter to Data Mining Research? 

    Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2006)
    Data mining (DM) and knowledge discovery are intelligent tools that help to accumulate and process data and make use of it. We review several existing frameworks for DM research that originate from different paradigms. ...
  • Dynamic Integration of Classifiers for Tracking Concept Drift in Antibiotic Resistance Data 

    Tsymbal, Alexey; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2005-02-17)
    In the real world concepts are often not stable but change with time. A typical example of this in the medical context is antibiotic resistance, where pathogen sensitivity may change over time as new pathogen strains ...
  • Dynamic Integration of Classifiers in the Space of Principal Components 

    Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2003)
    Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. It was shown that, for an ensemble to be successful, it should consist of ...
  • Dynamic Integration of Regression Models 

    Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2004-03-26)
    In this paper we adapt the recently proposed Dynamic Integration ensemble techniques for regression problems and compare their performance to the base models and to the popular ensemble technique of Stacked Regression. We ...
  • Dynamic Integration with Random Forests 

    Tsymbal, Alexey; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2006)
    Random Forests are a successful ensemble prediction technique that combines two sources of randomness to generate base decision trees; bootstrapping instances for each tree and considering a random subset of features ...
  • Feature Extraction for Classification in Knowledge 

    Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2003)
    Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of ?the curse ...
  • Feature Extraction for Dynamic Integration of Classifiers 

    Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2006)
    Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. In this paper, we present an algorithm for the dynamic integration of ...
  • Knowledge Discovery in Microbiology Data: Analysis of Antibiotic Resistance in Nosocomial Infections 

    Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2005)
    The goal of this paper is to address the currently serious problem of antibiotic resistance applying knowledge discovery techniques to real hospital data. In this paper we introduce our approach to that problem and the ...
  • Meta-Knowledge Management in MultiStrategy Process-Oriented Knowledge Discovery Systems 

    Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2005)
  • On the use of Information Systems Research Methods in Data Mining 

    Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2005)
  • Random subspacing for regression ensembles 

    Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2004)
    In this work we present a novel approach to ensemble learning for regression models, by combining the ensemble generation technique of random subspace method with the ensemble integration methods of Stacked Regression ...
  • A Review of Explanation and Explanation in Case-Based Reasoning 

    Doyle, Donal; Tsymbal, Alexey; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2003)
  • Search Strategies for Ensemble Feature Selection in Medical Diagnostics 

    Tsymbal, Alexey; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2003)
    The goal of this paper is to propose, evaluate, and compare four search strategies for ensemble feature selection, and to consider their application to medical diagnostics, with a focus on the problem of the classification ...
  • Sequential Genetic Search for Ensemble Feature Selection 

    Tsymbal, Alexey; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2005)
    Ensemble learning constitutes one of the main directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. One technique, which proved to ...