Communications of the ACM - Special issue on parallelism
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
A New Version of Rough Set Exploration System
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Hierarchical Neural Networks for Image Interpretation (Lecture Notes in Computer Science)
Hierarchical Neural Networks for Image Interpretation (Lecture Notes in Computer Science)
RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning
Fundamenta Informaticae
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
A Lattice machine approach to automated casebase design: marrying lazy and eager learning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Information systems in modeling interactive computations on granules
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Multimodal classification: case studies
Transactions on Rough Sets V
Reduced attribute oriented inconsistency handling in decision generation
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Interactive information systems: Toward perception based computing
Theoretical Computer Science
Dynamic Programming Approach for Partial Decision Rule Optimization
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
CHIRA---Convex Hull Based Iterative Algorithm of Rules Aggregation
Fundamenta Informaticae
Redefinition of Decision Rules Based on the Importance of Elementary Conditions Evaluation
Fundamenta Informaticae
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Data models that are induced in classifier construction often consists of multiple parts, each of which explains part of the data. Classification methods for such models are called the multimodal classification methods. The model parts may overlap or have insufficient coverage. How to deal best with the problems of overlapping and insufficient coverage? In this paper we propose hierarchical or layered approach to this problem. Rather than seeking a single model, we consider a series of models under gradually relaxing conditions, which form a hierarchical structure. To demonstrate the effectiveness of this approach we implemented it in two classifiers that construct multi-part models: one based on the so-called lattice machine and the other one based on rough set rule induction. This leads to hierarchical versions of the classifiers. The classification performance of these two hierarchical classifiers is compared with C4.5, Support Vector Machine (SVM), rule based classifiers (with the optimisation of rule shortening) implemented in Rough Set Exploration System (RSES), and a method combining k-nn with rough set rule induction (RIONA in RSES). The results of the experiments show that this hierarchical approach leads to improved multimodal classifiers.