Supporting the Process of Monument Classification Based on Reducts, Decision Rules and Neural Networks

2014 ◽  
pp. 327-334 ◽  
Author(s):  
Robert Olszewski ◽  
Anna Fiedukowicz
Keyword(s):  
Neural Networks ◽  
Decision Rules

scholarly journals Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic Inferences

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Benjamin W. Y. Lo ◽  
R. Loch Macdonald ◽  
Andrew Baker ◽  
Mitchell A. H. Levine
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Subarachnoid Hemorrhage ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Hidden Variables ◽  
Decision Rules ◽  
Bayesian Neural Networks ◽  
Prognostic System ◽  
History Of ◽  
Prediction Approach

Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH).Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients).Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique) denoted cut-off points for poor prognosis at greater than 2.5 clusters.Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication.

scholarly journals Deep Neural Networks Constrained by Decision Rules

2019 ◽  
Vol 33 ◽  
pp. 2496-2505
Author(s):  
Yuzuru Okajima ◽  
Kunihiko Sadamasa
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Predictive Accuracy ◽  
Decision Rules ◽  
Hybrid Technique ◽  
Complex Data ◽  
Rule Based ◽  
Prior Probabilities ◽  
The Neural Network ◽  
Latent Representations

Deep neural networks achieve high predictive accuracy by learning latent representations of complex data. However, the reasoning behind their decisions is difficult for humans to understand. On the other hand, rule-based approaches are able to justify the decisions by showing the decision rules leading to them, but they have relatively low accuracy. To improve the interpretability of neural networks, several techniques provide post-hoc explanations of decisions made by neural networks, but they cannot guarantee that the decisions are always explained in a simple form like decision rules because their explanations are generated after the decisions are made by neural networks.In this paper, to balance the accuracy of neural networks and the interpretability of decision rules, we propose a hybrid technique called rule-constrained networks, namely, neural networks that make decisions by selecting decision rules from a given ruleset. Because the networks are forced to make decisions based on decision rules, it is guaranteed that every decision is supported by a decision rule. Furthermore, we propose a technique to jointly optimize the neural network and the ruleset from which the network select rules. The log likelihood of correct classifications is maximized under a model with hyper parameters about the ruleset size and the prior probabilities of rules being selected. This feature makes it possible to limit the ruleset size or prioritize human-made rules over automatically acquired rules for promoting the interpretability of the output. Experiments on datasets of time-series and sentiment classification showed rule-constrained networks achieved accuracy as high as that achieved by original neural networks and significantly higher than that achieved by existing rule-based models, while presenting decision rules supporting the decisions.

NeuroLinear: From neural networks to oblique decision rules

1997 ◽  
Vol 17 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Rudy Setiono ◽  
Huan Liu
Keyword(s):  
Neural Networks ◽  
Decision Rules

ROUGH SETS AND GENETIC ALGORITHMS IN LEARNING CELLULAR NEURAL NETWORKS CLONING TEMPLATE FOR DECISION MAKING SYSTEM

2004 ◽  
Vol 14 (01) ◽  
pp. 57-68 ◽  
Author(s):  
ELSAYED RADWAN ◽  
EIICHIRO TAZAKI
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Rough Sets ◽  
Decision Rules ◽  
Cellular Neural Networks ◽  
Minimal Set ◽  
Rough Sets Theory ◽  
Decision Making System ◽  
Sets Theory

We purpose to find a new beneficial method for accelerating the Decision-Making and classifier support applied on imprecise data. This acceleration can be done by integration between Rough Sets theory, which gives us the minimal set of decision rules, and the Cellular Neural Networks. Our method depends on Genetic Algorithms for designing the cloning template for more accuracy. Some illustrative examples are given to demonstrate the effectiveness of the proposed method, whose advantages and limitations are also discussed.

scholarly journals Artificial intelligence in a rugged design based on multi-bit rules

2021 ◽  
Vol 2094 (3) ◽  
pp. 032009
Author(s):  
T A Zolotareva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Rule ◽  
Decision Rules ◽  
Single Layer ◽  
Image Data ◽  
National Standard ◽  
Output Code ◽  
Trained Neural Network ◽  
Single Layer Network

Abstract In this paper, the technologies for training large artificial neural networks are considered: the first technology is based on the use of multilayer “deep” neural networks; the second technology involves the use of a “wide” single-layer network of neurons giving 256 private binary solutions. A list of attacks aimed at the simplest one-bit neural network decision rule is given: knowledge extraction attacks and software data modification attacks; their content is considered. All single-bit decision rules are unsafe for applying. It is necessary to use other decision rules. The security of applying neural network decision rules in relation to deliberate hacker attacks is significantly reduced if you use a decision rule of a large number of output bits. The most important property of neural network transducers is that when it is trained using 20 examples of the “Friend” image, the “Friend” output code of 256 bits long is correctly reproduced with a confidence level of 0.95. This means that the entropy of the “Friend” output codes is close to zero. A well-trained neural network virtually eliminates the ambiguity of the “Friend” image data. On the contrary, for the “Foe” images, their initial natural entropy is enhanced by the neural network. The considered works made it possible to create a draft of the second national standard for automatic training of networks of quadratic neurons with multilevel quantizers.

scholarly journals Explainable Neural Network Ensembles

2021 ◽  
Author(s):  
Manomita Chakraborty ◽  
Saroj Kumar Biswas ◽  
Biswajit Purkayastha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Performance ◽  
Decision Rules ◽  
Rule Extraction ◽  
Classification Performance ◽  
Classification Rules ◽  
Multiple Networks ◽  
Neural Network Ensembles ◽  
Network Ensembles

Abstract Neural networks are known for providing impressive classification performance, and the ensemble learning technique is further acting as a catalyst to enhance this performance by integrating multiple networks. But like neural networks, neural network ensembles are also considered as a black-box because they cannot explain their decision making process. So, despite having high classification performance, neural networks and their ensembles are not suited for some applications which require explainable decisions. However, the rule extraction technique can overcome this drawback by representing the knowledge learned by a neural network in the guise of interpretable decision rules. A rule extraction algorithm provides neural networks with the power to justify their classification responses through explainable classification rules. Several rule extraction algorithms exist to extract classification rules from neural networks, but only a few of them generates rules using neural network ensembles. So this paper proposes an algorithm named Rule Extraction using Ensemble of Neural Network Ensembles (RE-E-NNES) to demonstrate the high performance of neural network ensembles through rule extraction. RE-E-NNES extracts classification rules by ensembling several neural network ensembles. Results show the efficacy of the proposed RE-E-NNES algorithm compared to different existing rule extraction algorithms.

Identifying Diagnostic Errors with Induced Decision Trees

2001 ◽  
Vol 21 (5) ◽  
pp. 368-375 ◽  
Author(s):  
Catherine K. Murphy
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Decision Trees ◽  
Cancer Diagnosis ◽  
Decision Rules ◽  
Diagnostic Errors ◽  
Breast Cancer Diagnosis ◽  
Data Set ◽  
Minority Class ◽  
New Methods

Objective. The purpose of this article is to compare the diagnostic accuracy of induced decision trees with that of pruned neural networks and to improve the accuracy and interpretation of breast cancer diagnosis from readings of thin-needle aspirate by identifying cases likely to be misclassified by induced decision rules. Method. Using an online database consisting of 699 cases of suspected breast cancer and their corresponding readings of fine-needle aspirate, decision trees were induced from half of the cases, randomly selected. Accuracy was determined for the remaining cases in successive partitions. The pattern of errors in the multiple decision trees was examined. A smaller data set was created with 2 classes: (1) correctly classified and (2) misclassified by a decision tree, rather than the original benign and malignant classes. From this data set, decision trees that describe the misclassified cases were induced. Results. Larger, less severely pruned decision trees were more accurate in breast cancer diagnosis for both training and test data. The accuracy of the induced decision trees exceeded that reported for the smaller pruned neural networks. Combining classifications from 2 trees was effective in identifying malignancies missed by a single tree. Induced decision trees were able to identify patterns associated with misclassified cases, but the identification of errors inductively did not improve the overall error rate. Conclusion. In this application, a model that is too compact identifies fewer cases of the minority class, malignancy. New methods that combine models and examine classification errors can improve diagnosis by identifying more malignancies and by describing ambiguous cases.

scholarly journals A NEW FRAMEWORK FOR GEOSPATIAL SITE SELECTION USING ARTIFICIAL NEURAL NETWORKS AS DECISION RULES: A CASE STUDY ON LANDFILL SITES

2015 ◽  
Vol II-2/W2 ◽  
pp. 131-138 ◽  
Author(s):  
S. K. M. Abujayyab ◽  
M. A. S. Ahamad ◽  
A. S. Yahya ◽  
A.-M. H. Y. Saad
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Site Selection ◽  
Confusion Matrix ◽  
Decision Rules ◽  
Site Characteristics ◽  
Feed Forward Network ◽  
New Locations ◽  
Artificial Neural ◽  
Current Characteristics

This paper briefly introduced the theory and framework of geospatial site selection (GSS) and discussed the application and framework of artificial neural networks (ANNs). The related literature on the use of ANNs as decision rules in GSS is scarce from 2000 till 2015. As this study found, ANNs are not only adaptable to dynamic changes but also capable of improving the objectivity of acquisition in GSS, reducing time consumption, and providing high validation. ANNs make for a powerful tool for solving geospatial decision-making problems by enabling geospatial decision makers to implement their constraints and imprecise concepts. This tool offers a way to represent and handle uncertainty. Specifically, ANNs are decision rules implemented to enhance conventional GSS frameworks. The main assumption in implementing ANNs in GSS is that the current characteristics of existing sites are indicative of the degree of suitability of new locations with similar characteristics. GSS requires several input criteria that embody specific requirements and the desired site characteristics, which could contribute to geospatial sites. In this study, the proposed framework consists of four stages for implementing ANNs in GSS. A multilayer feed-forward network with a backpropagation algorithm was used to train the networks from prior sites to assess, generalize, and evaluate the outputs on the basis of the inputs for the new sites. Two metrics, namely, confusion matrix and receiver operating characteristic tests, were utilized to achieve high accuracy and validation. Results proved that ANNs provide reasonable and efficient results as an accurate and inexpensive quantitative technique for GSS.

Determination of S-N curves with the application of artificial neural networks

1999 ◽  
Vol 22 (8) ◽  
pp. 723-728 ◽  
Author(s):  
Artymiak ◽  
Bukowski ◽  
Feliks ◽  
Narberhaus ◽  
Zenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural
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