WEKA: a machine learning workbench
Published 17 December 2002Open access
Geoffrey Holmes, A. Donkin, Ian H. Witten
Citations936
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
WEKA is a workbench for machine learning that is intended to aid in the application of machine learning techniques to a variety of real-world problems, in particular, those arising from agricultural and horticultural domains.
Abstract
WEKA is a workbench for machine learning that is intended to aid in the application of machine learning techniques to a variety of real-world problems, in particular, those arising from agricultural and horticultural domains. Unlike other machine learning projects, the emphasis is on providing a working environment for the domain specialist rather than the machine learning expert. Lessons learned include the necessity of providing a wealth of interactive tools for data manipulation, result visualization, database linkage, and cross-validation and comparison of rule sets, to complement the basic machine learning tools.>
Keywords
Computer Science
C4.5: Programs for Machine Learning
23,665 Citations1992J. R. Quinlan
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.
Programs for Machine Learning
5,794 Citations1994Steven L. Salzberg, Alberto M. Segre
In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments, which will be a welcome addition to the library of many researchers and students.
Machine LearningKnowledge Acquisition Via Incremental Conceptual Clustering
1,754 Citations1987Douglas Fisher
COBWEB is a conceptual clustering system that organizes data so as to maximize inference ability, and is incremental and computationally economical, and thus can be flexibly applied in a variety of domains.
Machine LearningKnowledge acquisition via incremental conceptual clustering
1,081 Citations1987Douglas Fisher
COBWEB is a conceptual clustering system that organizes data so as to maximize inference ability, and is incremental and computationally economical, and thus can be flexibly applied in a variety of domains.
International Journal of Man-Machine StudiesPRISM: An algorithm for inducing modular rules
652 Citations1987Jadzia Cendrowska
A new algorithm is described, PRISM, which, although based on ID3, uses a different induction strategy to induce rules which are modular, thus avoiding many of the problems associated with decision trees.
Lecture notes in computer scienceFOIL: A midterm report
513 Citations1993J. R. Quinlan, R. M. Cameron-Jones
This paper summarises the development of FOIL from 1989 up to early 1993 and evaluates its effectiveness on a non-trivial sequence of learning tasks taken from a Prolog programming text.
Elsevier eBooksAutoClass: A Bayesian Classification System
504 Citations1988Peter Cheeseman, James G. Kelly +4 more
Wiley series in probability and statisticsLISP‐STAT: An Object‐Oriented Environment for Statistical Computing and Dynamic Graphics
282 Citations1990Luke Tierney
Artificial IntelligenceConcept learning and heuristic classification in weak-theory domains
255 Citations1990Bruce Porter, Ray Bareiss +1 more
This paper describes a successful approach to concept learning for heuristic classification that has been applied to the domain of clinical audiology and achieved a competence level equaling that of human experts and far surpassing that of other machine learning programs.
MLC++: a machine learning library in C++
197 Citations2002Ron Kohavi, George H. John +3 more
The problems M LC++ aims to solve, the design of MLC++, and the current functionality are discussed, including the attempt to extract commonalities of algorithms and decompose them for a unified view that is simple, coherent, and extensible.
OC1: randomized induction of oblique decision trees
124 Citations1993Sreerama K. Murthy, Simon Kasif +2 more
Machine LearningEncouraging Experimental Results on Learning CNF
34 Citations1995Raymond J. Mooney
The CNF learner performs surprisingly well, and results on five natural data sets indicates that it frequently trains faster and produces more accurate and simpler concepts.
Machine LearningEncouraging experimental results on learning CNF
14 Citations1995Raymond J. Mooney
The CNF learner performs surprisingly well, and results on five natural data sets indicates that it frequently trains faster and produces more accurate and simpler concepts.
Geometric comparison of classifications and rule sets
5 Citations1994Trevor J. Monk, Rory Mitchell +2 more
This work presents a technique for evaluating classifications by geometric comparison of rule sets, which can be applied to classifications generated by different algorithms, with different numbers of classes and different attribute sets.