Open Access
Learning decision rules using a distributed evolutionary algorithm
Wojciech Kwedlo,Marek Kretowski +1 more
- 01 Jan 2002
pp 483-492
TL;DR: An implementation of EDRL-MD system in the cluster of multiprocessor machines connected by Fast Ethernet is described, showing that for large datasets this approach is able to obtain a significant speed-up in comparison to a single processor version.
read more
Abstract: A new parallel method for learning decision rules from databases by using an evolutionary algorithm is proposed. We describe an implementation of EDRL-MD system in the cluster of multiprocessor machines connected by Fast Ethernet. Our approach consists in a distribution of the learning set into processors of the cluster. The evolutionary algorithm uses a master-slave model to compute the fitness function in parallel. The remainder of evolutionary algorithm is executed in the master node. The experimental results show, that for large datasets our approach is able to obtain a significant speed-up in comparison to a single processor version.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
•Journal Article
Mining Classification Rules by Using Genetic Algorithms with Non-random Initial Population and Uniform Operator
TL;DR: From the experimental results, it was observed that, this method handled the problems of GAs in the task of classication and guaranteed to get rid of any local solution and rapidly found comprehensible rules.
Using Data-Mining Technique for Census Analysis to Give Geo- Spatial Distribution of Nigeria.
C. Okeke
- 01 Jan 2013
TL;DR: This paper is an effort towards harnessing the power of data-mining technique to develop mining model applicable to the analysis of census data that could uncover some hidden patterns to get their geo-spatial distribution.
4
A Parallel Evolutionary Algorithm for Discovery of Decision Rules
Wojciech Kwedlo
- 07 Sep 2003
TL;DR: In this article, a new parallel method for learning decision rules is proposed, which uses evolutionary algorithm to discover decision rules from datasets and describes a parallelization of the algorithm based on master-slave model.
Patent
Distributed rule-based probabilistic time-series classifier
Hodjat Babak,Shahrzad Hormoz +1 more
- 12 Oct 2017
TL;DR: In this paper, a genetic algorithm is used to derive a ruleset which predicts the probability of a particular outcome, and the rule probabilities are combined with the rule-level certainty values to derive the probability output for the ruleset, which can be used to provide a basis for decisions.
1
Global Induction of Decision Trees: From Parallel Implementation to Distributed Evolution
Marek Kretowski,Piotr Popczyński +1 more
- 22 Jun 2006
TL;DR: This paper developed Global Decision Tree (GDT) system, which learns a tree structure and tests in one run of the evolutionary algorithms, and investigates how the GDTsystem can profit from a parallelization on a compute cluster.
References
•Book
C4.5: Programs for Machine Learning
J. Ross Quinlan
- 15 Oct 1992
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
27.2K
•Book
Genetic Algorithms
David E. Goldberg,William Shakespeare +1 more
- 01 Jan 2002
TL;DR: The present work expresses the problem as a multi-objective optimization problem and a methodology has been proposed based on multi-objective genetic algo-rithm (MOGA) that exploits the effectiveness of MOGA for searching global optimal solutions in selecting an appropriate image enhancement operator.
17.1K
•Book
Genetic Algorithms + Data Structures = Evolution Programs
Zbigniew Michalewicz
- 01 Jan 1992
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
13.5K
•Book
MPI: The Complete Reference
Marc Snir,Steve W. Otto,David W. Walker,Jack Dongarra,Steven Huss-Lederman +4 more
- 01 Jan 1996
TL;DR: MPI: The Complete Reference is an annotated manual for the latest 1.1 version of the standard that illuminates the more advanced and subtle features of MPI and covers such advanced issues in parallel computing and programming as true portability, deadlock, high-performance message passing, and libraries for distributed and parallel computing.
2.8K