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Glass Identification Dataset

Overview

This dataset contains tabular data for classifying different types of glass based on their chemical properties. 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:

Glass-Identification/ train_data/ class_1/ sample_0.txt sample_1.txt ... class_2/ sample_0.txt sample_1.txt ... test_data/ class_1/ sample_0.txt sample_1.txt ... class_2/ sample_0.txt sample_1.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 multiple classes, each represented by a separate folder based on the type of glass.

Usage (pre-split; optimal parameters)

Here is an example of how to load the dataset using PrismRCL:

C:\PrismRCL\PrismRCL.exe chisquared rclticks=18 boxdown=1 channelpick=5 data=C:\path\to\Glass-Identification\train_data testdata=C:\path\to\Glass-Identification\test_data savemodel=C:\path\to\models\mymodel.classify log=C:\path\to\log_files stopwhendone

Explanation:

  • 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\Glass-Identification\train_data: path to training data
  • testdata=C:\path\to\Glass-Identification\test_data: 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.

Additional Information

The data values have been prepared to ensure compatibility with PrismRCL. No normalization is required as of version 2.4.0.