license: cc-by-4.0
Haberman's Survival Dataset
Overview
This dataset contains tabular data for classifying survival status of patients who had undergone surgery for breast cancer. Each sample is stored in a separate text file, with features space-separated on a single line. The dataset is structured to be compatible with Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application or API.
Dataset Structure
The dataset is organized into the following structure: Haberman-Survival/ train_data/ class_1/ sample_0.txt sample_10.txt ... class_2/ sample_0.txt sample_10.txt ... test_data/ class_1/ sample_0.txt sample_10.txt ... class_2/ sample_0.txt sample_10.txt ...
Note: All text file names must be unique across all class folders.
Features
- Tabular Data: Each text file contains space-separated values representing the features of a sample.
- Classes: There are two classes, each represented by a separate folder:
class_1: Patients who survived 5 years or longer.class_2: Patients who did not survive 5 years.
Usage(pre-split; optimal parameters)
Here is an example of how to load the dataset using PrismRCL:
C:\PrismRCL\PrismRCL.exe naivebayes rclticks=18 boxdown=1 channelpick=5 data=C:\path\to\Haberman-Survival\train_data testdata=C:\path\to\Haberman-Survival\test_data savemodel=C:\path\to\models\mymodel.classify log=C:\path\to\log_files stopwhendone
Explanation of Command
C:\PrismRCL\PrismRCL.exe: classification application
chisquared: training evaluation method
rclticks=18: RCL training parameter
boxdown=1: RCL training parameter
channelpick=5 : RCL training parameter
data=C:\path\to\Haberman-Survival\train: path to training data
testdata=C:\path\to\Haberman-Survival\test: path to testing data
savemodel=C:\path\to\models\mymodel.classify: path to save resulting model
log=C:\path\to\log_files: path to logfiles
stopwhendone: ends the PrismRCL session when training is done
License
This dataset is licensed under the Creative Commons Attribution 4.0 International License. See the LICENSE file for more details.
Original Source
This dataset was originally sourced from the UCI Machine Learning Repository. Please cite the original source if you use this dataset in your research or applications.
Haberman, S.J. (1976). Generalized Residuals for Log-Linear Models. Proceedings of the 9th International Biometrics Conference, Boston, 1976.
Additional Information
The data values have been prepared to ensure compatibility with PrismRCL. No normalization is required as of version 2.4.0.