On learning algorithm selection for classification
This paper introduces a new method for learning algorithm evaluation and selection, with
empirical results based on classification. The empirical study has been conducted among 8 …
empirical results based on classification. The empirical study has been conducted among 8 …
Improving the practice of classifier performance assessment
NM Adams, DJ Hand - Neural computation, 2000 - direct.mit.edu
In this note we use examples from the literature to illustrate some poor practices in
assessing the performance of supervised classification rules, and we suggest guidelines for …
assessing the performance of supervised classification rules, and we suggest guidelines for …
Addressing the selective superiority problem: Automatic algorithm/model class selection
CE Brodley - Proceedings of the tenth international conference …, 1993 - books.google.com
The results of empirical comparisons of existing learning algorithms illustrate that each
algorithm has a selective superiority; it is best for some but not all tasks. Given a data set, it is …
algorithm has a selective superiority; it is best for some but not all tasks. Given a data set, it is …
A comparison of ranking methods for classification algorithm selection
PB Brazdil, C Soares - Machine Learning: ECML 2000: 11th European …, 2000 - Springer
We investigate the problem of using past performance information to select an algorithm for
a given classification problem. We present three ranking methods for that purpose: average …
a given classification problem. We present three ranking methods for that purpose: average …
Fast algorithm selection using learning curves
JN van Rijn, SM Abdulrahman, P Brazdil… - Advances in Intelligent …, 2015 - Springer
One of the challenges in Machine Learning to find a classifier and parameter settings that
work well on a given dataset. Evaluating all possible combinations typically takes too much …
work well on a given dataset. Evaluating all possible combinations typically takes too much …
Accuracy measures for the comparison of classifiers
The selection of the best classification algorithm for a given dataset is a very widespread
problem. It is also a complex one, in the sense it requires to make several important …
problem. It is also a complex one, in the sense it requires to make several important …
Recursive automatic bias selection for classifier construction
CE Brodley - Machine Learning, 1995 - Springer
The results of empirical comparisons of existing learning algorithms illustrate that each
algorithm has a selective superiority; each is best for some but not all tasks. Given a data set …
algorithm has a selective superiority; each is best for some but not all tasks. Given a data set …
What is a learning classifier system?
What Is a Learning Classifier System? Page 1 What Is a Learning Classifier System? John H.
Holland1, Lashon B. Booker2, Marco Colombetti3, Marco Dorigo4, David E. Goldberg5 …
Holland1, Lashon B. Booker2, Marco Colombetti3, Marco Dorigo4, David E. Goldberg5 …
On comparing classifiers: Pitfalls to avoid and a recommended approach
SL Salzberg - Data mining and knowledge discovery, 1997 - Springer
An important component of many data mining projects is finding a good classification
algorithm, a process that requires very careful thought about experimental design. If not …
algorithm, a process that requires very careful thought about experimental design. If not …
Meta analysis of classification algorithms for pattern recognition
SY Sohn - IEEE transactions on pattern analysis and machine …, 1999 - ieeexplore.ieee.org
Various classification algorithms became available due to a surge of interdisciplinary
research interests in the areas of data mining and knowledge discovery. We develop a …
research interests in the areas of data mining and knowledge discovery. We develop a …