More Applications of Multi-stage Optimization of Decision Trees

2018 ◽  
pp. 73-84
Author(s):  
Hassan AbouEisha ◽  
Talha Amin ◽  
Igor Chikalov ◽  
Shahid Hussain ◽  
Mikhail Moshkov
Keyword(s):  
Decision Trees ◽  
Multi Stage

scholarly journals Efficiency of machine learning algorithms and convolutional neural network for detection of pathological changes in MR images of the brain

2020 ◽  
Vol 44 (2) ◽  
pp. 266-273 ◽  
Author(s):  
Yu.D. Agafonova ◽  
A.V. Gaidel ◽  
P.M. Zelter ◽  
A.V. Kapishnikov
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Decision Trees ◽  
Machine Learning Algorithms ◽  
Brain Diseases ◽  
Pathological Changes ◽  
Resonance Imaging ◽  
Multi Stage

We compare approaches for the automatic detection of pathological changes in brain MRI images that are visible to the naked eye. We analyse multi-stage approaches based on deep learning and threshold processing. A convolutional neural network was formed, a classifier was built based on the use of an ensemble of decision trees, and an algorithm was created for multi-stage image processing. Because of experimental studies, it was found that the most effective method for recognizing images of magnetic resonance imaging is an approach based on an ensemble of decision trees. With its help, 95 % of the images from the test sample were classified correctly. At the same time, using the convolutional neural network, it was possible to classify correctly all images containing the area of pathological changes. The data obtained can be used in practice for the diagnosis of brain diseases, for automating the processing of a large number of studies of magnetic resonance imaging.

Planning and Search in Multi-Stage Risky Decision Making

2011 ◽  
Author(s):  
Jared Hotaling ◽  
Jerry Busemeyer ◽  
Richard Shiffrin
Keyword(s):  
Decision Making ◽  
Risky Decision Making ◽  
Risky Decision ◽  
Multi Stage

Optimizing Diagnostic Test Sequences: The Probability Modifying Plot

1999 ◽  
Vol 38 (01) ◽  
pp. 50-55 ◽  
Author(s):  
P. F. de Vries Robbé ◽  
A. L. M. Verbeek ◽  
J. L. Severens
Keyword(s):  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Tract Infection ◽  
Decision Trees ◽  
Diagnostic Tests ◽  
Test Sequence ◽  
Optimal Sequence ◽  
Specific Test ◽  
Total Cost ◽  
Modelling Techniques

Abstract:The problem of deciding the optimal sequence of diagnostic tests can be structured in decision trees, but unmanageable bushy decision trees result when the sequence of two or more tests is investigated. Most modelling techniques include tests on the basis of gain in certainty. The aim of this study was to explore a model for optimizing the sequence of diagnostic tests based on efficiency criteria. The probability modifying plot shows, when in a specific test sequence further testing is redundant and which costs are involved. In this way different sequences can be compared. The model is illustrated with data on urinary tract infection. The sequence of diagnostic tests was optimized on the basis of efficiency, which was either defined as the test sequence with the least number of tests or the least total cost for testing. Further research on the model is needed to handle current limitations.

Automated Development of Clinical Strategies Using Multistage Decision Analysis

1986 ◽  
Vol 25 (04) ◽  
pp. 207-214 ◽  
Author(s):  
P. Glasziou
Keyword(s):  
Decision Tree ◽  
Decision Analysis ◽  
Decision Trees ◽  
Optimal Strategy ◽  
Cholestatic Jaundice ◽  
Clinical Strategies ◽  
Simultaneous Study

SummaryThe development of investigative strategies by decision analysis has been achieved by explicitly drawing the decision tree, either by hand or on computer. This paper discusses the feasibility of automatically generating and analysing decision trees from a description of the investigations and the treatment problem. The investigation of cholestatic jaundice is used to illustrate the technique.Methods to decrease the number of calculations required are presented. It is shown that this method makes practical the simultaneous study of at least half a dozen investigations. However, some new problems arise due to the possible complexity of the resulting optimal strategy. If protocol errors and delays due to testing are considered, simpler strategies become desirable. Generation and assessment of these simpler strategies are discussed with examples.

Generation of Correlated Logistic-Normal Random Variates for Medical Decision Trees

1998 ◽  
Vol 37 (03) ◽  
pp. 235-238 ◽  
Author(s):  
M. El-Taha ◽  
D. E. Clark
Keyword(s):  
Monte Carlo ◽  
Decision Trees ◽  
Computer Algebra ◽  
Taylor Series ◽  
Computer Algebra System ◽  
Random Variable ◽  
Monte Carlo Analysis ◽  
Normal Random Variable ◽  
Medical Decision ◽  
Theoretical Results

AbstractA Logistic-Normal random variable (Y) is obtained from a Normal random variable (X) by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may be used to model the branching probabilities. In some cases, the probabilities to be modeled may not be independent, and a method for generating correlated Logistic-Normal random variates would be useful. A technique for generating correlated Normal random variates has been previously described. Using Taylor Series approximations and the algebraic definitions of variance and covariance, we describe methods for estimating the means, variances, and covariances of Normal random variates which, after translation using the above formula, will result in Logistic-Normal random variates having approximately the desired means, variances, and covariances. Multiple simulations of the method using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.

Effects of Cooking Oil Subsidy Removals on Related Markets in Malaysia: A Comparative Static, Multicommodity, Multi Stage Production Approach

2011 ◽  
Author(s):  
Jamal Othman ◽  
Yaghoob Jafari
Keyword(s):  
Equilibrium Model ◽  
Comparative Statics ◽  
Partial Equilibrium ◽  
Cooking Oil ◽  
Multi Stage ◽  
Supply Factor ◽  
Partial Equilibrium Model ◽  
Factor Markets ◽  
Input Markets ◽  
Production Approach

Malaysia is contemplating removal of most of her subsidy support measures including subsidies on cooking oil which is largely palm oil based. This paper aims to examine the effects of cooking oil subsidy removals on the competitiveness of the oil palm subsector and related markets. This is done by developing and applying a comparative static, multi-commodity, partial equilibrium model with multi-stages of production function for the Malaysian perennial crops subsector which explicitly links different stages of production, primary and intermediate input markets, trade, and policy linkages. Results partly suggest that export of cooking oil will increase by 0.2 per cent due to a 10 per cent cooking oil subsidy reduction, while domestic output of cooking oil may eventually see a net decline of 1.97 per cent. The results clearly point out that the effect of reducing cooking oil subsidies is relatively small at the upstream levels and therefore it only induces minute effects on factor markets. Consequently, the market for other agricultural crops is projected to change very marginally.   Keywords: Multicomodity, comparative statics, partial equilibrium model, output supply-factor markets linkages, effects of cooking oil subsidy removals.

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