A New Incremental Algorithm for Induction of Multivariate Decision Trees for Large Datasets

2008 ◽  
pp. 282-289 ◽  
Author(s):  
Anilu Franco-Arcega ◽  
J. Ariel Carrasco-Ochoa ◽  
Guillermo Sánchez-Díaz ◽  
J. Fco Martínez-Trinidad
Keyword(s):  
Decision Trees ◽  
Large Datasets ◽  
Incremental Algorithm

Multivariate Decision Trees Using Different Splitting Attribute Subsets for Large Datasets

2010 ◽  
pp. 370-373 ◽  
Author(s):  
Anilu Franco-Arcega ◽  
José Ariel Carrasco-Ochoa ◽  
Guillermo Sánchez-Díaz ◽  
José Fco. Martínez-Trinidad
Keyword(s):  
Decision Trees ◽  
Large Datasets

Parallel discrepancy detection and incremental detection

2021 ◽  
Vol 14 (8) ◽  
pp. 1351-1364
Author(s):  
Wenfei Fan ◽  
Chao Tian ◽  
Yanghao Wang ◽  
Qiang Yin
Keyword(s):  
Machine Learning ◽  
Real Life ◽  
Large Datasets ◽  
Incremental Algorithm ◽  
Complexity Bounds ◽  
Np Complete ◽  
Synthetic Datasets ◽  
Carry Over ◽  
Discrepancy Detection ◽  
Special Case

This paper studies how to catch duplicates, mismatches and conflicts in the same process. We adopt a class of entity enhancing rules that embed machine learning predicates, unify entity resolution and conflict resolution, and are collectively defined across multiple relations. We detect discrepancies as violations of such rules. We establish the complexity of discrepancy detection and incremental detection problems with the rules; they are both NP-complete and W[1]-hard. To cope with the intractability and scale with large datasets, we develop parallel algorithms and parallel incremental algorithms for discrepancy detection. We show that both algorithms are parallelly scalable, i.e. , they guarantee to reduce runtime when more processors are used. Moreover, the parallel incremental algorithm is relatively bounded. The complexity bounds and algorithms carry over to denial constraints, a special case of the entity enhancing rules. Using real-life and synthetic datasets, we experimentally verify the effectiveness, scalability and efficiency of the algorithms.

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.

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