1. Decision tree ensembles in biomedical time-series classifaction
Alan Jović1, Karla Brkić1, Nikola Bogunović1
1 University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, Zagreb, Croatia,
{alan.jovic, karla.brkic,
Transformations and feature extraction
Biomedical time-series
Biomedical time-series datasets
Transformations:
Characteristics:
Fourier transform
Hilbert transform
Wavelet transform
Binary class or multiclass
From several features to several hundred features
Feature vectors numbers vary
Very few open, referential datasets available
Biomedical time-series prepared datasets
Features:
Morphological
Statistical
Frequency
Time-frequency
Nonlinear
+ Personal data
Difficult results comparison:
Different data
Different disorders
Different classifiers
Goal: Demonstrate the potential of decision tree ensembles in biomedical time series classification, compare to SVM – still preliminary results
Three datasets
Seven classifiers
Classification results
Arrhythmia dataset (UCI repository)
- 13 classes, 279 features, 452 instances
AdaBoost+C4.5 (AB)
MultiBoost+C4.5 (MB)
Random forest (RF)
Rotation forest (RTF)
SVM SMO-based
- Linear
- Squared polynomial
- Radial
HRV-based arrhythmia (PhysioNet, two databases) (HRV)
- 9 classes, 230 features, 8843 instances
HRV-based heart disorder (PhysioNet, six databases) (CHF)
- 3 classes (normal, arrhytmic, CHF),
237 features, 3317 instances
Statistically significant win/loss/tie, α=0.05, Student’s paired t-test for 9x10-fold crossvalidation (first 10-fold iteration used for finding optimal model parameters)
Conclusion
Preliminary results strongly support the use of decision tree ensembles to improve model accuracy in biomedical time-series classification,
especially AdaBoost+C4.5 and MultiBoost+C4.5.
Further investigations are necessary.
Average classification model construction times (in seconds) for the three datasets