Communications of the ACM - Special issue on parallelism
Rough set algorithms in classification problem
Rough set methods and applications
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Boolean Reasoning for Decision Rules Generation
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
A New Version of Rough Set Exploration System
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Nearest Neighbors by Neighborhood Counting
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A hierarchical approach to multimodal classification
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
The rough set exploration system
Transactions on Rough Sets III
Analogy-based reasoning in classifier construction
Transactions on Rough Sets IV
Building a highly-compact and accurate associative classifier
Applied Intelligence
Application of the Method of Editing and Condensing in the Process of Global Decision-making
Fundamenta Informaticae
Decision rule-based data models using TRS and NetTRS – methods and algorithms
Transactions on Rough Sets XI
A Rough Set Approach to Information Systems Decomposition
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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Data models that are induced in classifier construction often consist of multiple parts, each of which explains part of the data. Classification methods for such multi-part models are called 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 a 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 consider 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, and we design hierarchical versions of the two classifiers. The two hierarchical classifiers are compared through experiments with their non-hierarchical counterparts, and also with a method that combines k-nearest neighbors classifier with rough set rule induction as a benchmark. The results of the experiments show that this hierarchical approach leads to improved multimodal classifiers.