Compact Data Learning for Machine Learning Classifications

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-02-21 DOI:10.3390/axioms13030137

S. Kim

引用次数: 1

Abstract

This paper targets the area of optimizing machine learning (ML) training data by constructing compact data. The methods of optimizing ML training have improved and become a part of artificial intelligence (AI) system development. Compact data learning (CDL) is an alternative practical framework to optimize a classification system by reducing the size of the training dataset. CDL originated from compact data design, which provides the best assets without handling complex big data. CDL is a dedicated framework for improving the speed of the machine learning training phase without affecting the accuracy of the system. The performance of an ML-based arrhythmia detection system and its variants with CDL maintained the same statistical accuracy. ML training with CDL could be maximized by applying an 85% reduced input dataset, which indicated that a trained ML system could have the same statistical accuracy by only using 15% of the original training dataset.

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机器学习分类的紧凑型数据学习

本文旨在通过构建紧凑数据来优化机器学习（ML）训练数据。优化机器学习（ML）训练的方法已经得到改进，并成为人工智能（AI）系统开发的一部分。紧凑数据学习（CDL）是通过缩小训练数据集来优化分类系统的另一种实用框架。CDL 源于紧凑型数据设计，它无需处理复杂的大数据就能提供最佳资产。CDL 是一种专用框架，可在不影响系统准确性的情况下提高机器学习训练阶段的速度。使用 CDL 的基于 ML 的心律失常检测系统及其变体的性能保持了相同的统计精度。使用 CDL 进行的 ML 训练可通过减少 85% 的输入数据集实现最大化，这表明只需使用原始训练数据集的 15%，经过训练的 ML 系统就能获得相同的统计精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.