C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Efficient mining of association rules using closed itemset lattices
Information Systems
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Formalizing Hypotheses with Concepts
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
Quantization of Continuous Input Variables for Binary Classification
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
TRIAS--An Algorithm for Mining Iceberg Tri-Lattices
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Database Systems: The Complete Book
Database Systems: The Complete Book
A New Search Results Clustering Algorithm Based on Formal Concept Analysis
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Learning Markov logic network structure via hypergraph lifting
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Topological properties of concept spaces (full version)
Information and Computation
Semi-Supervised Learning
Mining gene expression data with pattern structures in formal concept analysis
Information Sciences: an International Journal
Learning closed sets of labeled graphs for chemical applications
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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Only few machine learning methods; e.g., the decision tree-based classification method, can handle mixed-type data sets containing both of discrete (binary and nominal) and continuous (real-valued) variables and, moreover, no semi-supervised learning method can treat such data sets directly. Here we propose a novel semi-supervised learning method, called SELF (SEmi-supervised Learning via FCA), for mixed-type data sets using Formal Concept Analysis (FCA). SELF extracts a lattice structure via FCA together with discretizing continuous variables and learns classification rules using the structure effectively. Incomplete data sets including missing values can be handled directly in our method. We experimentally demonstrate competitive performance of SELF compared to other supervised and semi-supervised learning methods. Our contribution is not only giving a novel semi-supervised learning method, but also bridging two fields of conceptual analysis and knowledge discovery.