# decision table

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Related to decision table: Decision tree

## 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.

## 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.
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.
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.
Traverso, "A tool for specification analysis: complete decision tables," IFAC Proceedings Volumes, vol.
Specifically, the condition attributes of decision table are set to X = {X1 = area, X2 = perimeter, X3 = elongation, X4 = rectangle degree, X5 = first-order invariant moments, X6 = second-order moment invariants, X7 = third-order moment invariants, X8 = fourth-order moment invariants, X9 = fifth-order invariant moments, X10 = sixth-order invariant moments, X11 = seventh-order invariant moments, X12 = mean, X13 = variance, X14 = gray entropy, X15 = energy, X16 = inertance, X17 = consistency, X18 = roughness, X19 = contrast, X20 = direction}.
Algorithm Records Increase AUC Naive Bayes 91 40% 0.95 Logistic regression 98 50.77% 0.96 lazy.Kstar 82 26.15% 0.97 Decision Table 103 58.46% 0.89 Random Forest 107 64.62% 0.96 Algorithm Best performing attributes Naive Bayes ALT, albumin, pararenal shunt, liver stiffness, spleen stiffness Logistic regression Albumin, pararenal shunt, liver stiffness lazy.Kstar Umbilical shunt, pararenal shunt, hepatic venous blood flow type, splenic vein width, liver stiffness Decision Table Albumin, liver stiffness Random Forest Age, albumin, pararenal shunt, liver stiffness, spleen stiffness TABLE 4: The results of all four algorithms test scenarios.
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:
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.
The proposed framework is constructed by creating the medical decision table based on Rough-Mereology concepts.
In this section, we introduce the concepts of decision table, discretization algorithm, and information entropy in RS theory and explain their relationships.
In RS, data is organized in a table called decision table, containing attributes as columns and data elements as rows.
The decision table is established reasonably through discretizing attribution values.

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