Artificial neural networks (ANN) have demonstrated good predictive performance in a wide variety of real world problems. However, there are strong arguments as to why ANNs are insufficient for data mining. The arguments are the poor comprehensibility of the learned ANNs, which is the inability to represent the learned knowledge in an understandable way to the users. In this paper, Artificial Neural Network Tree (ANNT), i.e. ANN training preceded by Decision Tree rules extraction method, is presented to overcome the comprehensibility problem of ANN. Experimental results on three data sets show that the proposed algorithm generates rules that are better than C4.5. This paper provides an evaluation of the proposed method in terms of accuracy, ...
Although backpropagation ANNs generally predict better than decision trees do for pattern classifica...
In this paper we present comparative study of two frequently used methods for prediction and classif...
Artificial neural networks are widely spread models that outperform more basic, but explainable mach...
A major drawback associated with the use of artificial neural networks for data mining is their lack...
A major drawback associated with the use of artificial neural networks for data mining is their lack...
Neural networks have been successfully applied in a wide range of supervised and unsupervised learni...
Artificial Neural Networks (ANNs) have proved both a pop-ular and powerful technique for pattern rec...
Abstract — Companies have been collecting data for decades, building massive data warehouses in whic...
Artificial Neural Networks (ANNs) have proved both a popular and powerful technique for pattern rec...
Inside the sets of data, hidden knowledge can be acquired by using neural network. These knowledge a...
Active research into processes and techniques for extracting the knowledge embedded within trained a...
Data mining a multidisciplinary field, is an analytic process designed to explore data (typically bu...
A common problem in Data Mining (DM) is the presence of noise in the data being mined. Artificial ne...
It is becoming increasingly apparent that, without some form of explanation capability, the full pot...
Data Mining accomplishes nontrivial extraction of implicit, previously unknown, and potentially usef...
Although backpropagation ANNs generally predict better than decision trees do for pattern classifica...
In this paper we present comparative study of two frequently used methods for prediction and classif...
Artificial neural networks are widely spread models that outperform more basic, but explainable mach...
A major drawback associated with the use of artificial neural networks for data mining is their lack...
A major drawback associated with the use of artificial neural networks for data mining is their lack...
Neural networks have been successfully applied in a wide range of supervised and unsupervised learni...
Artificial Neural Networks (ANNs) have proved both a pop-ular and powerful technique for pattern rec...
Abstract — Companies have been collecting data for decades, building massive data warehouses in whic...
Artificial Neural Networks (ANNs) have proved both a popular and powerful technique for pattern rec...
Inside the sets of data, hidden knowledge can be acquired by using neural network. These knowledge a...
Active research into processes and techniques for extracting the knowledge embedded within trained a...
Data mining a multidisciplinary field, is an analytic process designed to explore data (typically bu...
A common problem in Data Mining (DM) is the presence of noise in the data being mined. Artificial ne...
It is becoming increasingly apparent that, without some form of explanation capability, the full pot...
Data Mining accomplishes nontrivial extraction of implicit, previously unknown, and potentially usef...
Although backpropagation ANNs generally predict better than decision trees do for pattern classifica...
In this paper we present comparative study of two frequently used methods for prediction and classif...
Artificial neural networks are widely spread models that outperform more basic, but explainable mach...
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