Hybrid inductive machine learning: an overview of CLIP algorithms

Authors:
Krzysztof J. Cios;Łukasz A. Kurgan
Affiliations:
Univ. of Colorado at Denver, Denver;Univ. of Colorado at Denver, Denver
Venue:
New learning paradigms in soft computing
Year:
2002

Citing 14
Cited 8

Conceptual clustering and its relation to numerical taxonomy

Artificial intelligence and statistics
Operations research: principles and practice, 2nd ed.

Operations research: principles and practice, 2nd ed.
Rule induction with CN2: some recent improvements

EWSL-91 Proceedings of the European working session on learning on Machine learning
Learning flexible concepts: fundamental ideas and a method based on two-tiered representation

Machine learning
Where's the AI?

AI Magazine
C4.5: programs for machine learning

C4.5: programs for machine learning
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets

Machine Learning
Multivariate Decision Trees

Machine Learning
Elements of machine learning

Elements of machine learning
Inductive machine learning for instrument development

Information and Management
Data mining methods for knowledge discovery

Data mining methods for knowledge discovery
The CN2 Induction Algorithm

Machine Learning
A Brief Introduction to Boosting

IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning from Inconsistent and Noisy Data: The AQ18 Approach

ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems

CAIM Discretization Algorithm

IEEE Transactions on Knowledge and Data Engineering
CLIP4: hybrid inductive machine learning algorithm that generates inequality rules

Information Sciences: an International Journal - Special issue: Soft computing data mining
A discretization algorithm based on Class-Attribute Contingency Coefficient

Information Sciences: an International Journal
A discretization algorithm for uncertain data

DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Review:

The Knowledge Engineering Review
Inductive learning models with missing values

Mathematical and Computer Modelling: An International Journal
Mining of classification patterns in clinical data through data mining algorithms

Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Mutual information based input feature selection for classification problems

Decision Support Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The chapter describes inductive machine learning methods for generating hypotheses about given training data. It focuses on hybrid algorithms that generate hypotheses in the form of production ifthenrules, which constitute the model of the data.