Knowledge Discovery Support from a Meningoencephalitis Dataset Using an Automatic Composition Tool for Inductive Applications

Authors:
Hiromitsu Hatazawa;Hidenao Abe;Mao Komori;Yoshiaki Tachibana;Takahira Yamaguchi
Affiliations:
-;-;-;-;-
Venue:
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Year:
2001

Citing 4
Cited 5

Classifier systems and genetic algorithms

Artificial Intelligence
C4.5: programs for machine learning

C4.5: programs for machine learning
Induction of Decision Trees

Machine Learning
Bagging, boosting, and C4.S

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1

Constructing Inductive Applications by Meta-Learning with Method Repositories

Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Evaluation of rule interestingness measures in medical knowledge discovery in databases

Artificial Intelligence in Medicine
Evaluating a rule evaluation support method based on objective rule evaluation indices

PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Developing a rule evaluation support method based on objective indices

RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Evaluating learning models for a rule evaluation support method based on objective indices

RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Here is presented CAMLET that is a platform for automatic composition of inductive applications using ontologies that specify inductive learning methods. CAMLET constructs inductive applications using process and object ontologies. We have applied CAMLET to a meningoencephalitis dataset and evaluated CAMLET. The experimental results have shown us that it supports a human expert in discovering knowledge interesting to him.