Add to ORKGThis article proposes a method for achieving an appropriate balance between the parameters of support, precision, and complexity during the evolution of classification rules by means of genetic programming. The method includes an adaptive procedure in order to achieve such balance. This work lies within the data mining context, more precisely, it focuses on the extraction of comprehensible knowledge where the approach introduced plays a predominant role. Experimental results demonstrate the advantages of using the proposed method
The process of automatically extracting novel, useful and ultimately comprehensible information from...
The use of machine learning techniques to automatically analyse data for information is becoming inc...
Data mining is an important process, with applications found in many business, science and industria...
This article proposes a method for achieving an appropriate balance between the parameters of suppor...
Abstract: Genetic Programming (GP) has been emerged as a promising approach to deal with classificat...
Abstract: This chapter discusses the use of evolutionary algorithms, particularly genetic algorithms...
Classification is one of the most researchable ideas in machine learning and data mining. A wide ran...
In data mining, the quality of induced knowledge is determined by several features. The focus has be...
Classification rule mining from huge amount of data is a challenging issue in data mining. Classific...
This Thesis addresses the task of feature construction for classification. The quality of the data i...
Abstract—Classification is one of the most researched questions in machine learning and data mining....
Genetic algorithms are one of the most commonly used approaches in data mining. In this article, we ...
In essence, data mining consists of extracting knowledge from data. This paper proposes a co-evoluti...
In this paper, Genetic Programming is used to evolveordered rule sets (also called decision lists) f...
Recent experiments with a genetic-based encoding schema are presented as a potentially useful tool i...
The process of automatically extracting novel, useful and ultimately comprehensible information from...
The use of machine learning techniques to automatically analyse data for information is becoming inc...
Data mining is an important process, with applications found in many business, science and industria...
This article proposes a method for achieving an appropriate balance between the parameters of suppor...
Abstract: Genetic Programming (GP) has been emerged as a promising approach to deal with classificat...
Abstract: This chapter discusses the use of evolutionary algorithms, particularly genetic algorithms...
Classification is one of the most researchable ideas in machine learning and data mining. A wide ran...
In data mining, the quality of induced knowledge is determined by several features. The focus has be...
Classification rule mining from huge amount of data is a challenging issue in data mining. Classific...
This Thesis addresses the task of feature construction for classification. The quality of the data i...
Abstract—Classification is one of the most researched questions in machine learning and data mining....
Genetic algorithms are one of the most commonly used approaches in data mining. In this article, we ...
In essence, data mining consists of extracting knowledge from data. This paper proposes a co-evoluti...
In this paper, Genetic Programming is used to evolveordered rule sets (also called decision lists) f...
Recent experiments with a genetic-based encoding schema are presented as a potentially useful tool i...
The process of automatically extracting novel, useful and ultimately comprehensible information from...
The use of machine learning techniques to automatically analyse data for information is becoming inc...
Data mining is an important process, with applications found in many business, science and industria...