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Neural Computing and Applications

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01-04-2015 | Advances in Intelligent Data Processing and Analysis

Invasive weed classification

Authors: Roozbeh Razavi-Far, Vasile Palade, Enrico Zio

Published in: Neural Computing and Applications | Issue 3/2015

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Abstract

Invasive weed optimization (IWO) is a recently published heuristic optimization technique that resembles other evolutionary optimization methods. This paper proposes a new classification technique based on the IWO algorithm, called the invasive weed classification (IWC), to face the problem of pattern classification for multi-class datasets. The aim of the IWC is to find the set of the positions of the class centers that minimize the multi-objective function, i.e., the optimal positions of the class centers. The classification performance is computed as the percentage of misclassified patterns in the testing dataset achieved by the best plants in terms of fitness performance. The performance of the IWC algorithm, both in terms of classification accuracy and training time, is compared with other commonly used classification algorithms.

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Title: Invasive weed classification
Authors: Roozbeh Razavi-Far
Vasile Palade
Enrico Zio
Publication date: 01-04-2015
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 3/2015
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1656-3

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