Generating C4.5 production rules in parallel

Authors:
Richard Kufrin
Affiliations:
National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL
Venue:
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Year:
1997

Citing 8
Cited 7

Boolean Feature Discovery in Empirical Learning

Machine Learning
C4.5: programs for machine learning

C4.5: programs for machine learning
Applications of machine learning and rule induction

Communications of the ACM
Scaling up inductive learning with massive parallelism

Machine Learning
From data mining to knowledge discovery: an overview

Advances in knowledge discovery and data mining
Database Mining: A Performance Perspective

IEEE Transactions on Knowledge and Data Engineering
Generating production rules from decision trees

IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Scaling up: distributed machine learning with cooperation

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

A Survey of Methods for Scaling Up Inductive Algorithms

Data Mining and Knowledge Discovery
Parallelism in Knowledge Discovery Techniques

PARA '02 Proceedings of the 6th International Conference on Applied Parallel Computing Advanced Scientific Computing
Learning Rules from Distributed Data

Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
Data mining tasks and methods: scalability

Handbook of data mining and knowledge discovery
Data mining tasks and methods: parallel methods for scaling data mining algorithms to large data sets

Handbook of data mining and knowledge discovery
Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction

Journal of Management Information Systems - Special section: Data mining
Improving the scalability of ILP-based multi-relational concept discovery system through parallelization

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Induction systems that represent concepts in the form of production rules have proven to be useful in a variety of domains where both accuracy and comprehensibility of the resulting models are important. However, the computational requirements for inducing a set of rules from large, noisy training sets can be enormous, so that techniques for improving the performance of rule induction systems by exploiting parallelism are of considerable interest. Recent work to parallelize the C4.5 rule generator algorithm is described. After presenting an overview of the algorithm and the parallelization strategy employed, empirical results of the parallel implementation that demonstrate substantial speedup over serial execution are provided.