Table 4.
Comparison of algorithm performance.
| Classification Algorithm | Accuracy (% Correct) | Standard Deviation |
|---|---|---|
| Decision Trees | ||
| RandomForest | 96.51 | 0.59 |
| NBTree | 95.40 | 0.71 |
| J48graft | 94.93 | 0.77 |
| J48 | 94.83 | 0.81 |
| RandomTree | 94.45 | 0.93 |
| LMT | 93.25 | 1.29 |
| FT | 92.66 | 1.02 |
| BFTree | 93.77 | 0.83 |
| REPTree | 93.58 | 1.06 |
| LADTree | 85.37 | 1.29 |
| SimpleCart | 94.00 | 0.83 |
|
| ||
| Artificial Neural Networks | ||
| MultilayerPerceptron | 87.15 | 1.80 |
|
| ||
| Bayesian Techniques | ||
| NaiveBayes | 81.47 | 1.15 |
| BayesNet | 90.89 | 0.93 |
|
| ||
| Logistic Regression | ||
| Logistic | 83.41 | 1.05 |
|
| ||
| Support Vector Machines | ||
| SMO | 80.24 | 1.02 |
|
| ||
| Instance-Based Classifiers | ||
| KStar | 95.55 | 0.70 |
| IBk(kNN = 5) | 80.32 | 1.25 |
| LWL | 95.45 | 0.14 |
|
| ||
| Baseline | ||
| ZeroR | 40.52 | 0.06 |