C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Machine Learning
Simplifying Decision Trees
Mining students' behavior in web-based learning programs
Expert Systems with Applications: An International Journal
Mining learners' behavior in accessing web-based interface
Edutainment'07 Proceedings of the 2nd international conference on Technologies for e-learning and digital entertainment
A novel and effective method for web system tuning based on feature selection
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Examples of integration of induction and deduction in knowledge discovery
Reasoning, Action and Interaction in AI Theories and Systems
| Hi-index | 0.00 |
Classification is one of the most useful techniques for extracting meaningful knowledge from databases. Classifiers, e.g. decision trees, are usually extracted from a table of records, each of which represents an example. However, quite often in real applications there is other knowledge, e.g. owned by experts of the field, that can be usefully used in conjunction with the one hidden inside the examples. As a concrete example of this kind of knowledge we consider causal dependencies among the attributes of the data records. In this paper we discuss how to use such a knowledge to improve the construction of classifiers. The causal dependencies are represented via Bayesian Causal Maps (BCMs), and our method is implemented as an adaptation of the well known C4.5 algorithm.