Add to ORKGform ithm 8 alg We evaluate the algorithms ’ performance in terms of a variety of accuracy and complexity measures. Consistent with the No Free Applied Soft Computing 6 (2Lunch theorem, we do not expect to identify the single algorithm that performs best on all datasets. Rather, we aim to determine the characteristics of datasets that lend themselves to superior modelling by certain learning algorithms. Our empirical results are used to generate rules, using the rule-based learning algorithm C5.0, to describe which types of algorithms are suited to solving which types of classification problems. Most of the rules are generated with a high confidence rating
International audienceEmpirical performance evaluations, in competitions and scientific publications...
It is widely accepted that the empirical behavior of classifiers strongly depends on available data....
The No Free Lunch (NFL) Theorem imposes a theoretical restriction on optimization algorithms and the...
This paper introduces a new method for learning algorithm evaluation and selection, with empirical r...
As more data mining algorithms become available, the answer to one question becomes increasingly imp...
AbstractWe present ELEM2, a machine learning system that induces classification rules from a set of ...
Rule-based classifiers are supervised learning techniques that are extensively used in various domai...
Learning algorithms proved their ability to deal with large amount of data. Most of the statistical ...
wcohenresearchattcom Many existing rule learning systems are computationally expensive on large nois...
Machine learning algorithms are used to train the machine to learn on its own and improve from exper...
Data mining involves the computational process to find patterns from large data sets. Classification...
We discuss basic sample complexity theory and it's impact on classification success evaluation,...
In today’s world,enormous amount of data is available in every field including science, industry, bu...
Much research has been conducted in the area of machine learning algorithms; however, the question o...
Machine learning is an established method of selecting algorithms to solve hard search problems. Des...
International audienceEmpirical performance evaluations, in competitions and scientific publications...
It is widely accepted that the empirical behavior of classifiers strongly depends on available data....
The No Free Lunch (NFL) Theorem imposes a theoretical restriction on optimization algorithms and the...
This paper introduces a new method for learning algorithm evaluation and selection, with empirical r...
As more data mining algorithms become available, the answer to one question becomes increasingly imp...
AbstractWe present ELEM2, a machine learning system that induces classification rules from a set of ...
Rule-based classifiers are supervised learning techniques that are extensively used in various domai...
Learning algorithms proved their ability to deal with large amount of data. Most of the statistical ...
wcohenresearchattcom Many existing rule learning systems are computationally expensive on large nois...
Machine learning algorithms are used to train the machine to learn on its own and improve from exper...
Data mining involves the computational process to find patterns from large data sets. Classification...
We discuss basic sample complexity theory and it's impact on classification success evaluation,...
In today’s world,enormous amount of data is available in every field including science, industry, bu...
Much research has been conducted in the area of machine learning algorithms; however, the question o...
Machine learning is an established method of selecting algorithms to solve hard search problems. Des...
International audienceEmpirical performance evaluations, in competitions and scientific publications...
It is widely accepted that the empirical behavior of classifiers strongly depends on available data....
The No Free Lunch (NFL) Theorem imposes a theoretical restriction on optimization algorithms and the...