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Fast prototype selection algorithm based on adjacent neighbourhood and boundary approximation
1College of Distance Education, Shaanxi Normal University, Xi'an, 710062, Shaanxi, China.
Scientific Reports
|November 23, 2022
Summary
This study introduces a new prototype selection algorithm for large datasets. It efficiently creates a smaller, high-quality reference set for incremental learning without sacrificing accuracy.
Area of Science:
- Machine Learning
- Data Mining
- Computer Science
Background:
- Increasing data volumes challenge traditional classification algorithms.
- Existing methods struggle with execution time and memory constraints in incremental environments.
Purpose of the Study:
- To develop a novel prototype selection algorithm for efficient incremental learning.
- To address the need for fast, adaptive reference set generation in large datasets.
Main Methods:
- Integrates condensing and editing strategies for prototype selection.
- Extends neighbor reference from single to k-nearest neighborhood.
- Uses neighbor relationships and classification boundaries to identify prototypes.
- Periodically updates prototypes in non-boundary or unlearned zones.
Main Results:
- Achieves a smaller reference set with higher boundary prototypes.
- Maintains classification accuracy and reduction rate compared to existing algorithms.
- Demonstrates effectiveness in handling incremental data environments and large datasets.
Conclusions:
- The proposed algorithm offers an efficient solution for large-scale incremental learning.
- It effectively balances reference set size, accuracy, and adaptability.
- Provides a valuable tool for applications requiring dynamic data processing.
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