Machine Learning
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to remove Internet advertisements
Proceedings of the third annual conference on Autonomous Agents
A classification-based methodology for planning audit strategies in fraud detection
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge management and data mining for marketing
Decision Support Systems - Knowledge management support of decision making
Mastering Data Mining: The Art and Science of Customer Relationship Management
Mastering Data Mining: The Art and Science of Customer Relationship Management
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
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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
Feature Selection for Financial Credit-Risk Evaluation Decisions
INFORMS Journal on Computing
A Dynamic Programming Based Pruning Method for Decision Trees
INFORMS Journal on Computing
CMP: A Fast Decision Tree Classifier Using Multivariate Predictions
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Learning classifiers using hierarchically structured class taxonomies
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Expert Systems with Applications: An International Journal
A GRASP method for building classification trees
Expert Systems with Applications: An International Journal
Variable precision rough set based decision tree classifier
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Hybrid approaches for approximate reasoning
Hi-index | 12.05 |
Most decision tree classifiers are designed to classify the data with categorical or Boolean class labels. Unfortunately, many practical classification problems concern data with class labels that are naturally organized as a hierarchical structure, such as test scores. In the hierarchy, the ranges in the upper levels are less specific but easier to predict, while the ranges in the lower levels are more specific but harder to predict. To build a decision tree from this kind of data, we must consider how to classify data so that the class label can be as specific as possible while also ensuring the highest possible accuracy of the prediction. To the best of our knowledge, no previous research has considered the induction of decision trees from data with hierarchical class labels. This paper proposes a novel classification algorithm for learning decision tree classifiers from data with hierarchical class labels. Empirical results show that the proposed method is efficient and effective in both prediction accuracy and prediction specificity.