Evolutionary algorithm-optimized feature fusion for accurate classification of shredded tobacco using multi-sensor data.

ABSTRACT:

Introduction

Individual sensor systems have limitations in the complex task of classifying shredded tobacco. This study aims to overcome these limitations by developing a novel evolutionary algorithm-based feature fusion framework to enhance sensing accuracy.

Methods

We fused data from three sensing modalities: GC-SAW, E-nose, and FTIR. A systematic comparison was conducted to determine the optimal fusion strategy. Seven dimensionality reduction methods were rigorously evaluated, leading to the selection of a genetic algorithm (GA) as the cornerstone for feature selection within our fusion framework.

Results

Feature-level fusion was confirmed as the most effective strategy. The GA-based feature selection demonstrated exceptional performance, achieving a mean classific

SUBMITTER: Chen L 

PROVIDER: S-EPMC12832984 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature