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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: Path to the PrismRCL executable for classification
naivebayes: Specifies Naive Bayes as the training evaluation method
rclticks=18: Sets the number of RCL iterations during training to 18
boxdown=1: Configuration parameter for training behavior
channelpick=5: RCL training parameter
data=C:\path\to\Haberman-Survival\train_data: Path to the training data for Haberman Survival classification
testdata=C:\path\to\Haberman-Survival\test_data: Path to the testing data for evaluation
savemodel=C:\path\to\models\mymodel.classify: Path to save the resulting trained model
log=C:\path\to\log_files: Directory path for storing log files of the training process
stopwhendone: Instructs PrismRCL to end the session once training is complete

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.