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A fast supervised density-based discretization algorithm for classification tasks in the medical domain.

Aristos Aristodimou¹, Andreas Diavastos^2,3, Constantinos S Pattichis¹

¹Department of Computer Science, University of Cyprus, Nicosia, Cyprus.

Health Informatics Journal

|February 16, 2022

Summary

This summary is machine-generated.

A new density-based discretization algorithm (DBAD) efficiently converts continuous data to categorical. It performs comparably to existing methods while being significantly faster, with parallel versions showing excellent speedup for big data applications.

Keywords:

big data classification density estimation density-based discretization supervised discretization

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Area of Science:

Data Science
Machine Learning
Bioinformatics

Background:

Discretization is crucial for machine learning algorithms requiring categorical data.
Efficient discretization is vital in the big data era.

Purpose of the Study:

Introduce a novel supervised density-based discretization (DBAD) algorithm.
Evaluate DBAD's performance and efficiency against state-of-the-art methods.

Main Methods:

Developed a supervised density-based discretization (DBAD) algorithm.
Tested DBAD on 11 medical domain datasets against three established discretizers.
Evaluated a parallel DBAD version on synthetic big datasets using MPI and hybrid MPI/OpenMP.

Main Results:

DBAD demonstrated statistically similar or superior performance compared to existing algorithms in most tests.
DBAD was consistently faster than other discretizers.
Parallel DBAD achieved near-linear speedup (MPI) and significant execution time improvement (hybrid MPI/OpenMP).

Conclusions:

DBAD is an efficient and effective discretization technique for continuous data.
The parallel implementation of DBAD is suitable for big data processing.
DBAD offers a competitive alternative for data preprocessing in machine learning.