Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Evaluation and Selection of Biases in Machine Learning
Machine Learning - Special issue on bias evaluation and selection
Generalization-based data mining in object-oriented databases using an object cube model
Data & Knowledge Engineering - Special jubilee issue: DKE 25
Machine Learning
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
RainForest - A Framework for Fast Decision Tree Construction of Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Building Hierarchical Classifiers Using Class Proximity
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
An Interval Classifier for Database Mining Applications
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Scalable Mining for Classification Rules in Relational Databases
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Short communication: Data mining method for listed companies' financial distress prediction
Knowledge-Based Systems
Combine multi-valued attribute decomposition with multi-label learning
Expert Systems with Applications: An International Journal
Metrics on Multilabeled Trees: Interrelationships and Diameter Bounds
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Research on multi-valued and multi-labeled decision trees
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Decision trees: a recent overview
Artificial Intelligence Review
A Framework to Generate Synthetic Multi-label Datasets
Electronic Notes in Theoretical Computer Science (ENTCS)
Variable precision rough set based decision tree classifier
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Hybrid approaches for approximate reasoning
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We have proposed a decision tree classifier named MMC (multi-valued and multi-labeled classifier) before. MMC is known as its capability of classifying a large multi-valued and multi-labeled data. Aiming to improve the accuracy of MMC, this paper has developed another classifier named MMDT (multi-valued and multi-labeled decision tree). MMDT differs from MMC mainly in attribute selection. MMC attempts to split a node into child nodes whose records approach the same multiple labels. It basically measures the average similarity of labels of each child node to determine the goodness of each splitting attribute. MMDT, in contrast, uses another measuring strategy which considers not only the average similarity of labels of each child node but also the average appropriateness of labels of each child node. The new measuring strategy takes scoring approach to have a look-ahead measure of accuracy contribution of each attribute's splitting. The experimental results show that MMDT has improved the accuracy of MMC.