Add to ORKGA common problem when using complicated models for prediction and classification is that the complexity of the model entails that it is hard, or impossible, to interpret. For some scenarios this might not be a limitation, since the priority is the accuracy of the model. In other situations the limitations might be severe, since additional aspects are important to consider; e.g. comprehensibility or scalability of the model. In this study we show how the gap between accuracy and other aspects can be bridged by using a rule extraction method (termed G-REX) based on genetic programming. The extraction method is evaluated against the five criteria accuracy, comprehensibility, fidelity, scalability and generality. It is also shown how G-REX can ...
In data mining, the quality of induced knowledge is determined by several features. The focus has be...
4siMany risk-sensitive applications require Machine Learning (ML) models to be interpretable. Attemp...
Abstract—In many real problems the regression models have to be accurate but, also, interpretable in...
When performing predictive data mining, the use of ensembles is claimed to virtually guarantee incre...
When performing predictive data mining, the use of ensembles is claimed to virtually guarantee incre...
Most highly accurate predictive modeling techniques produce opaque models. When comprehensible model...
The purpose of this paper is to propose and evaluate a methodfor reducing the inherent tendency of g...
During the development of applied systems, an important problem that must be addressed is that of ch...
During the development of applied systems, an important problem that must be addressed is that of ch...
Explainable artificial intelligence has received great interest in the recent decade, due to its imp...
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...
John Koza7 has demonstrated that a form of machine learning can be constructed by using the techniqu...
In data mining, we emphasize the need for learning from huge, incomplete, and imperfect data sets. T...
This article proposes a method for achieving an appropriate balance between the parameters of suppor...
In data mining, the quality of induced knowledge is determined by several features. The focus has be...
4siMany risk-sensitive applications require Machine Learning (ML) models to be interpretable. Attemp...
Abstract—In many real problems the regression models have to be accurate but, also, interpretable in...
When performing predictive data mining, the use of ensembles is claimed to virtually guarantee incre...
When performing predictive data mining, the use of ensembles is claimed to virtually guarantee incre...
Most highly accurate predictive modeling techniques produce opaque models. When comprehensible model...
The purpose of this paper is to propose and evaluate a methodfor reducing the inherent tendency of g...
During the development of applied systems, an important problem that must be addressed is that of ch...
During the development of applied systems, an important problem that must be addressed is that of ch...
Explainable artificial intelligence has received great interest in the recent decade, due to its imp...
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...
John Koza7 has demonstrated that a form of machine learning can be constructed by using the techniqu...
In data mining, we emphasize the need for learning from huge, incomplete, and imperfect data sets. T...
This article proposes a method for achieving an appropriate balance between the parameters of suppor...
In data mining, the quality of induced knowledge is determined by several features. The focus has be...
4siMany risk-sensitive applications require Machine Learning (ML) models to be interpretable. Attemp...
Abstract—In many real problems the regression models have to be accurate but, also, interpretable in...