This paper describes RULES3-EXT, a new algorithm for inductive learning. It has been developed to cope with some drawbacks of RULES-3 induction algorithm. The extra features of RULES3-EXT are (1) The number of required files to extract a knowledge base (a set of rules) is reduced to 2 from 3 (2) The repeated examples are eliminated, (3) The users are able to change the order of attributes and (4) The system is able to fire rule(s) partially if any of the extracted rules cannot fully be satisfied by an unseen example. The new algorithm has been tested on well known data sets and the efficiency found to be superior to that of RULES-3
An important area of application for machine learning is in automating the acquisition of knowledge ...
In most data-mining applications where induction is used as the primary tool for knowledge extractio...
A new system for learning by induction, called Lab, is presented. Lab (Learning for ABduction) lear...
In recent years, there has been a growing amount of research on inductive learning. Out of this rese...
RULES-3 Plus is a member of the RULES family of simple inductive learning algorithms with successful...
In recent years, there has been a growing amount of research on inductive learning. Out of this rese...
In recent years, there has been a growing amount of research on inductive learning. Out of this rese...
Data mining has been recognized as a key research topic in database systems and machine learning. It...
Inductive learning enables the system to recognize patterns and regularities in previous knowledge o...
Machine learning programs can automatically learn to recognise complex patterns and make intelligen...
ii Machine learning programs can automatically learn to recognise complex patterns and make intellig...
Symbolic inductive learning systems that induce concept descriptions from examples are valuable tool...
. This paper describes a multi-layer incremental induction algorithm, MLII, which is linked to an ex...
The RULES family of algorithms is reviewed in this work and the drawback of the variation in their g...
When learning is based on noisy data, the induced rule sets have a tendency to overfit the training ...
An important area of application for machine learning is in automating the acquisition of knowledge ...
In most data-mining applications where induction is used as the primary tool for knowledge extractio...
A new system for learning by induction, called Lab, is presented. Lab (Learning for ABduction) lear...
In recent years, there has been a growing amount of research on inductive learning. Out of this rese...
RULES-3 Plus is a member of the RULES family of simple inductive learning algorithms with successful...
In recent years, there has been a growing amount of research on inductive learning. Out of this rese...
In recent years, there has been a growing amount of research on inductive learning. Out of this rese...
Data mining has been recognized as a key research topic in database systems and machine learning. It...
Inductive learning enables the system to recognize patterns and regularities in previous knowledge o...
Machine learning programs can automatically learn to recognise complex patterns and make intelligen...
ii Machine learning programs can automatically learn to recognise complex patterns and make intellig...
Symbolic inductive learning systems that induce concept descriptions from examples are valuable tool...
. This paper describes a multi-layer incremental induction algorithm, MLII, which is linked to an ex...
The RULES family of algorithms is reviewed in this work and the drawback of the variation in their g...
When learning is based on noisy data, the induced rule sets have a tendency to overfit the training ...
An important area of application for machine learning is in automating the acquisition of knowledge ...
In most data-mining applications where induction is used as the primary tool for knowledge extractio...
A new system for learning by induction, called Lab, is presented. Lab (Learning for ABduction) lear...