Logistic Model Tree Extraction From Artificial Neural Networks
نویسندگان
چکیده
منابع مشابه
Knowledge Extraction from Artificial Neural Networks Models
The paper describes the development and application of several techniques for knowledge extraction from trained ANN models, such as the identification of redundant inputs and hidden neurons, deriving of causal relationships between inputs and outputs, and analysis of the hidden neuron behavior in classification ANN. Example of the application of these techniques is given of the faulty LED displ...
متن کاملDecision Tree Extraction from Trained Neural Networks
Artificial Neural Networks (ANNs) have proved both a popular and powerful technique for pattern recognition tasks in a number of problem domains. However, the adoption of ANNs in many areas has been impeded, due to their inability to explain how they came to their conclusion, or show in a readily comprehendible form the knowledge they have obtained. This paper presents an algorithm that address...
متن کاملTransmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model
BACKGROUND The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors. METHODOLOGY/PRINCIPAL FINDINGS We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression mod...
متن کاملArtificial neural networks versus bivariate logistic regression in prediction diagnosis of patients with hypertension and diabetes
Background: Diabetes and hypertension are important non-communicable diseases and their prevalence is important for health authorities. The aim of this study was to determine the predictive precision of the bivariate Logistic Regression (LR) and Artificial Neutral Network (ANN) in concurrent diagnosis of diabetes and hypertension. Methods: This cross-sectional study was performed with 12000 ...
متن کاملmonthly rainfall prediction using artificial neural networks and m5 model tree (case study: station of ahar)
introduction rainfall is considered as one of the most important factures in water cycle. prediction of monthly rainfall is important for many purposes such as estimating torrent, drought, run-off, sediment, irrigation programming and also management of drainage basins. rainfall prediction in each area is mediated by punctual data measured as humidity, temperature, wind speed and etc. as iran i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)
سال: 2007
ISSN: 1083-4419
DOI: 10.1109/tsmcb.2007.895334