C4.5: programs for machine learning
C4.5: programs for machine learning
A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
Generalization and decision tree induction: efficient classification in data mining
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
On retrieval from a small version of a large data base
VLDB '80 Proceedings of the sixth international conference on Very Large Data Bases - Volume 6
Lessons and Challenges from Mining Retail E-Commerce Data
Machine Learning
Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data
Knowledge and Information Systems
Multi-dimensional features reduction of PCA on SVM classifier for imaging surveillance application
ISPRA'08 Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Cost-sensitive Iterative Abductive Reasoning with abstractions
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Application of Support Vector Machine classifier for security surveillance system
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
Multi-granularity classification rule discovery using ERID
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Learning ontology-aware classifiers
DS'05 Proceedings of the 8th international conference on Discovery Science
Syntactic pattern recognition from observations: a hybrid technique
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
An iterative approach to build relevant ontology-aware data-driven models
Information Sciences: an International Journal
| Hi-index | 0.00 |
Most learning algorithms for data-driven induction of pattern classifiers (e.g., the decision tree algorithm), typically represent input patterns at a single level of abstraction - usually in the form of an ordered tuple of attribute values. However, in many applications of inductive learning - e.g., scientific discovery, users often need to explore a data set at multiple levels of abstraction, and from different points of view. Each point of view corresponds to a set of ontological (and representational) commitments regarding the domain of interest. The choice of an ontology induces a set of representatios of the data and a set of transformations of the hypothesis space. This paper formalizes the problem of inductive learning using ontologies and data; describes an ontology-driven decision tree learning algorithm to learn classification rules at multiple levels of abstraction; and presents preliminary results to demonstrate the feasibility of the proposed approach.