Decision Trees and Data Preprocessing to Help Clustering Interpretation

2014 ◽  
Author(s):  
Olivier Parisot ◽  
Mohammad Ghoniem ◽  
Benoît Otjacques
Keyword(s):  
Decision Trees ◽  
Data Preprocessing

Development and selection of decision trees for water management: Impact of data preprocessing, algorithms and settings

2016 ◽  
Vol 29 (6) ◽  
pp. 711-723 ◽  
Author(s):  
Gert Everaert ◽  
Ine Pauwels ◽  
Elina Bennetsen ◽  
Peter L.M. Goethals
Keyword(s):  
Water Management ◽  
Decision Trees ◽  
Data Preprocessing ◽  
Selection Of

Computer-aided analysis of data from evaluation sheets of subjects with autism spectrum disorders

2016 ◽  
Vol 12 (3) ◽  
Author(s):  
Krzysztof Pancerz ◽  
Aneta Derkacz ◽  
Olga Mich ◽  
Jerzy Gomula
Keyword(s):  
Autism Spectrum Disorders ◽  
Decision Trees ◽  
Data Preprocessing ◽  
Autism Spectrum ◽  
Classification Rules ◽  
Initial Analysis ◽  
Computer Aided Analysis ◽  
Computer Aided ◽  
Spectrum Disorders ◽  
Evaluation Sheet

AbstractIn this paper, we deal with the problem of the initial analysis of data from evaluation sheets of subjects with autism spectrum disorders (ASDs). In the research, we use an original evaluation sheet including questions about competencies grouped into 17 spheres. An initial analysis is focused on the data preprocessing step including the filtration of cases based on consistency factors. This approach enables us to obtain simpler classifiers in terms of their size (a number of nodes and leaves in decision trees and a number of classification rules).

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.

Context dependent modeling of phones in continuous speech using decision trees

1991 ◽  
Author(s):  
L. R. Bahl ◽  
P. V. de Soutza ◽  
P. S. Gopalakrishnan ◽  
D. Nahamoo ◽  
M. A. Picheny
Keyword(s):  
Decision Trees ◽  
Continuous Speech ◽  
Context Dependent
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