This technical report briefly describes our recent work in the iterative rule learning approach (IRL) of evolutionary learning/genetics-based machine learning. This approach was initiated by the SIA system. A more recent example is HIDER. Our approach integrates some of the main characteristics of GAssist, a system belonging to the Pittsburgh approach of Evolutionary Learning, into the general framework of IRL. Our aims in developing this system are use all the good characteristics of GAssist but at the same time overcome some of the scalability limitations that it presents
Abstract—This paper describes an approach based on evo-lutionary algorithms, hierarchical decision r...
Evolutionary Computation (EC) approaches are inspired by nature and solve optimization problems in a...
This chapter considers learning algorithms patterned after the processes underlying evolution: shapi...
This technical report briefly describes our recent work in the iterativerule learning approach (IRL)...
Currently, the data mining and machine learning fields are facing new challenges because of the amou...
Evolutionary learning techniques are comparable in accuracy with other learning methods such as Baye...
This paper describes an approach based on evolutionary algorithms, hierarchical decision rules (HID...
Abstract—The classification problem can be addressed by numerous techniques and algorithms which bel...
Initial experiments with a genetic based encoding schema are presented as a potentially powerful too...
This paper describes a new approach, HIerarchical DEcision Rules (HIDER), for learning generalizabl...
This book provides a unified framework that describes how genetic learning can be used to design pat...
Teaching experience shows that during educational process student perceive graphical information bet...
Some of the most influential factors in the quality of the solutions found by an evolutionary algor...
We present a new classification system based on Evolutionary Algorithm (EA), OBLIC. This tool is an ...
This paper describes a new approach, HIDER (HIerarchical DEcision Rules), for learning rules in con...
Abstract—This paper describes an approach based on evo-lutionary algorithms, hierarchical decision r...
Evolutionary Computation (EC) approaches are inspired by nature and solve optimization problems in a...
This chapter considers learning algorithms patterned after the processes underlying evolution: shapi...
This technical report briefly describes our recent work in the iterativerule learning approach (IRL)...
Currently, the data mining and machine learning fields are facing new challenges because of the amou...
Evolutionary learning techniques are comparable in accuracy with other learning methods such as Baye...
This paper describes an approach based on evolutionary algorithms, hierarchical decision rules (HID...
Abstract—The classification problem can be addressed by numerous techniques and algorithms which bel...
Initial experiments with a genetic based encoding schema are presented as a potentially powerful too...
This paper describes a new approach, HIerarchical DEcision Rules (HIDER), for learning generalizabl...
This book provides a unified framework that describes how genetic learning can be used to design pat...
Teaching experience shows that during educational process student perceive graphical information bet...
Some of the most influential factors in the quality of the solutions found by an evolutionary algor...
We present a new classification system based on Evolutionary Algorithm (EA), OBLIC. This tool is an ...
This paper describes a new approach, HIDER (HIerarchical DEcision Rules), for learning rules in con...
Abstract—This paper describes an approach based on evo-lutionary algorithms, hierarchical decision r...
Evolutionary Computation (EC) approaches are inspired by nature and solve optimization problems in a...
This chapter considers learning algorithms patterned after the processes underlying evolution: shapi...