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decision table

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decision table

[di′sizh·ən ‚tā·bəl]
(computer science)
A table of contingencies to be considered in the definition of a problem, together with the actions to be taken; sometimes used in place of a flow chart for program documentation.
DETAB
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.

decision table

A list of decisions and their criteria. Designed as a matrix, it lists criteria (inputs) and the results (outputs) of all possible combinations of the criteria. It can be placed into a program to direct its processing. By changing the decision table, the program is changed accordingly.
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References in periodicals archive
Conclusion: It was concluded that J48 algorithm was the best for predicting the students' grades as compared to Decision Table, OneR, Random Forest and Random Tree with the nine attributes related to students' profile and learning preferences.
AN EMPIRICAL STUDY OF MACHINE LEARNING ALGORITHMS TO PREDICT STUDENTS' GRADES
The second step was the application of algorithms CFS, Decision Table, REPTree, and M5P to describe the correlation between [C.sub.a] and wavelengths, individually.
Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification
Traverso, "A tool for specification analysis: complete decision tables," IFAC Proceedings Volumes, vol.
Model-Based Testing Applied to Software Components of Satellite Simulators
Definitions Decision table S=<U,R, V, f>, discourse domain U={[e.sub.i]|i=i, 2, ...m}, attribute set R=C[union]D, condition attribute C={[a.sub.i]|i = 1, 2, ...n}, decision attribute D={d}, [a.sub.i]([e.sub.j]) is the value of the object [e.sub.j] in the attribute [a.sub.i], [C.sub.D](i, j) represents the element of row i, column j in an identifiable matrix, and diseernibility matrix CD:
Research on government's response capability to network opinion on public emergencies: measurement model and empirical analysis
Anti-jamming decision table Condition Attribute C Decision Attribute D Schemes U Modulation Encoding Channel Power C1 C2 C3 (dBm) 1 BPSK 1/4 0 (0,20) 2,3,4,5,6,7 BPSK 1/4 1,2,3,4,5,6 0 8 QPSK 1/4 0 (2.9,20) 9 QPSK 1/4 1 (0,2.9] 10,11,12,13,14 QPSK 1/4 2,3,4,5,6 0 15 QPSK 1/2 0 (6.7,20) 16 QPSK 1/2 1 (3.8,6.7] 17 QPSK 1/2 2 (0,3.8] 18,19,20,21 QPSK 1/2 3,4,5,6 0 22 QPSK 4/5 0 (10.2,20) 23 QPSK 4/5 1 (7.3,10.2] 24 QPSK 4/5 2 (3.5,7.3] 25 QPSK 4/5 3 (0,3.5] 26,27,28 QPSK 4/5 4,5,6 0 29 8QAM 4/5 0 (16.4,20) 30 8QAM 4/5 1 (13.5,16.4] 31 8QAM 4/5 2 (9.7,13.5] 32 8QAM 4/5 3 (6.2,9.7] 33 8QAM 4/5 4 (0,6.2] 34,35 8QAM 4/5 5,6 0 36 32QAM 4/5 2 (17.2,20] 37 32QAM 4/5 3 (13.7,17.2] 38 32QAM 4/5 4 (7.5,13.7] 39 32QAM 4/5 5 (0,7.5] 40 32QAM 4/5 6 0 By utilizing the method of Sec.
A fast anti-jamming decision method based on the rule-reduced genetic algorithm
--generate an exhaustive set of rules consisting of all possible rules for a decision table,
Dominance-based rough set approach to network bridge management/Dominuojanciu apytiksliu aibiu metodo taikymas tiltu tinklo valdymui/Uz dominejosa stavokla aptuvena novertejuma metodes balstita tiklveida pieeja tiltu kopas parvaldibai/Dominantsuspohine ligikaudne lahenemine teedevorgu sildade hooldusel
In this section, we introduce the concepts of decision table, discretization algorithm, and information entropy in RS theory and explain their relationships.
Structural damage identification based on rough sets and artificial neural network
The decision table is established reasonably through discretizing attribution values.
A novel classification approach through integration of rough sets and back-propagation neural network
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