Table 2.
Machine-learning approaches
| Machine | Reference |
|---|---|
| Single machines | |
| Artificial neural networks (ANN) | Arminger and Enache (1996); Sarle (1994); Zou et al. (2008) |
| Diagonal linear discriminant analysis (DLDA) | Guo et al. (2007); McLachlan (2004) |
| k-nearest neighbors (kNN) | Steinbach and Tan (2009) |
| Linear discriminant analysis (LDA) | Guo et al. (2007); McLachlan (2004) |
| Logic regression | Chen et al. (2011); Schwender and Ruczinski (2010) |
| Logistic regression (logReg) | Hilbe (2009); Kleinbaum and Klein (2010) |
| Naïve Bayes | Hand (2009) |
| Quadratic discriminant analysis (QDA) | Guo et al. (2007); McLachlan (2004) |
| Support vector machines (SVM) | König et al. (2008); Noble (2006); Schölkopf and Smola (2002) |
| Tree-based methods: | Breiman et al. (1984) |
| C4.5 | Ramakrishnan (2009) |
| Classification trees | Steinberg (2009) |
| Logistic regression tree with unbiased selection (LOTUS) | Chan and Loh (2004); Loh (2011) |
| CRUISE, M5, QUEST | Loh (2011) |
| Probability estimation trees (PETs) | Provost and Domingos (2003); Steinberg (2009) |
| Regression trees | Steinberg (2009) |
| Ensemble machines | |
| Boosting | Hastie et al. (2009); König et al. (2008) |
| Bootstrap aggregation (bagging) | Breiman (1996); König et al. (2008) |
| Deterministic forest | Zhang et al. (2003) |
| Random forest (RF) | Breiman (2001); König et al. (2008); Malley et al. (2012); Schwarz et al. (2010) |