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Updated: Nov 15, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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An alternative approach to dimension reduction for pareto distributed data: a case study.

Marco Roccetti1, Giovanni Delnevo1, Luca Casini1

  • 1Department of Computer Science and Engineering, University of Bologna, Via Mura Anteo Zamboni 7, 40127 Bologna, Italy.

Journal of Big Data
|March 2, 2021
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Summary

Deep learning models can struggle with categorical data. A new approach using Pareto analysis to reshape data improved water meter failure detection accuracy to 87-90%.

Keywords:
BinningCategorical dataDataset coherence analysisDeep learning modelsImbalanced datasetsLearning space dimensionsMachine learningPareto analysisPrincipal component analysis

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Area of Science:

  • Machine Learning
  • Data Science
  • Engineering

Background:

  • Deep learning models approximate complex relationships for prediction.
  • Challenges exist in selecting optimal data descriptors for deep learning.
  • Categorical data can degrade deep learning model performance due to increased dimensionality.

Purpose of the Study:

  • To investigate the impact of categorical data on deep learning model accuracy for mechanical water meter failure detection.
  • To develop an improved strategy for incorporating categorical data into deep learning models.
  • To enhance the performance of deep learning models in identifying defective devices.

Main Methods:

  • Developed a deep learning model for water meter failure detection.
  • Experimented with incorporating categorical device descriptors.
  • Applied Pareto analysis to reshape the dataset using categorical data.
  • Retrained the deep learning model with the adjusted dataset.

Main Results:

  • Directly using categorical descriptors led to decreased prediction accuracy.
  • Alternative methods for dimensionality reduction or traditional algorithms were unsuccessful.
  • Reshaping the dataset based on Pareto analysis enabled a more performative deep learning model.
  • Achieved prediction accuracy in the range of 87-90% for detecting defective water meter devices.

Conclusions:

  • Categorical data can be effectively utilized in deep learning by employing data reshaping techniques like Pareto analysis.
  • This novel approach overcomes the limitations of directly inputting categorical data, enhancing model performance.
  • The refined deep learning model demonstrates significant improvements in detecting mechanical water meter failures.