Multivariate discretization for associative classification in a sparse data application domain

Authors:
María N. Moreno García;Joel Pinho Lucas;Vivian F. López Batista;M. José Polo Martín
Affiliations:
Department of Computing and Automatic, University of Salamanca, Salamanca, Spain;Department of Computing and Automatic, University of Salamanca, Salamanca, Spain;Department of Computing and Automatic, University of Salamanca, Salamanca, Spain;Department of Computing and Automatic, University of Salamanca, Salamanca, Spain
Venue:
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Year:
2010

Citing 14
Cited 0

Original Contribution: Stacked generalization

Neural Networks
Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Bagging predictors

Machine Learning
Mining quantitative association rules in large relational tables

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A Validation of the Component-Based Method for Software Size Estimation

IEEE Transactions on Software Engineering - special section on current trends in exception handling—part II
Discretization: An Enabling Technique

Data Mining and Knowledge Discovery
A Statistical Theory for Quantitative Association Rules

Journal of Intelligent Information Systems
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Building knowledge discovery-driven models for decision support in project management

Decision Support Systems
An association rule mining method for estimating the impact of project management policies on software quality, development time and effort

Expert Systems with Applications: An International Journal
MCAR: multi-class classification based on association rule

AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
A Novel Rule Ordering Approach in Classification Association Rule Mining

MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
An efficient algorithm for finding dense regions for mining quantitative association rules

Computers & Mathematics with Applications
Fuzzy versus quantitative association rules: a fair data-driven comparison

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Associative classification is becoming a promising alternative to classical machine learning algorithms It is a hybrid technique that combines supervised and unsupervised data mining algorithms and builds classifiers from association rules' models The aim of this work is to apply these associative classifiers to improve estimation precision in the project management area where data sparsity involves a major drawback Moreover, in this application domain, most of the attributes are continuous; therefore, they must be discretized before generating the rules The discretization procedure has a significant effect on the quality of the induced rules as well as on the precision of the classifiers built from them In this paper, a multivariate supervised discretization method is proposed, which takes into account the predictive purpose of the association rules.