Table 1 Data mining techniques for learning schemes
From: A genetic algorithm based framework for software effort prediction
DP | Id | Name | AS | Id | Name | LA | Id | Name |
|---|---|---|---|---|---|---|---|---|
1 | None | None | 1 | GS | GeneticSearch | 1 | GP | GaussianProcesses |
2 | Log | Logartihmic | 2 | BF | BestFirst | 2 | LMS | LeastMedSq |
3 | BC(-2) | Box-Cox(λ= -2) | 3 | LFS | LinearForwardSelection | 3 | LR | LinearRegression |
4 | BC(-1) | Box-Cox(λ= -1) | 4 | BE | Backward Elimination | 4 | MP | MultilayerPerceptron |
5 | BC(-.5) | Box-Cox(λ= -0.5) | 5 | FS | Forward Selection | 5 | RBFN | RBFNetwork |
6 | BC(.5) | Box-Cox(λ= 0.5) | 6 | SMO | SMOreg | |||
7 | BC(1) | Box-Cox(λ= 1) | 7 | AR | AdditiveRegression | |||
8 | BC(2) | Box-Cox(λ= 2) | 8 | BGN | Bagging | |||
9 | CR | ConjunctiveRule | ||||||
10 | DT | DecisionTable | ||||||
11 | M5R | M5Rules | ||||||
12 | ZR | ZeroR | ||||||
13 | DS | DecisionStump | ||||||
14 | M5P | M5P | ||||||
15 | RT | REPTree |