caa a22 4500 606159568 CHVBK 20210128100625.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s00521-014-1656-3 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1656-3 Invasive weed classification [Elektronische Daten] [Roozbeh Razavi-Far, Vasile Palade, Enrico Zio] 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. The Natural Computing Applications Forum, 2014 Pattern recognition nationallicence Invasive weed classification nationallicence Optimization nationallicence Razavi-Far Roozbeh Department of Energy, Politecnico di Milano, Via Ponzio 34/3, 20133, Milan, Italy aut Palade Vasile Faculty of Engineering and Computing, Coventry University, Priory Street, Coventry, UK aut Zio Enrico Department of Energy, Politecnico di Milano, Via Ponzio 34/3, 20133, Milan, Italy aut Neural Computing and Applications Springer London 26/3(2015-04-01), 525-539 0941-0643 26:3<525 2015 26 521 https://doi.org/10.1007/s00521-014-1656-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1656-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Razavi-Far Roozbeh Department of Energy, Politecnico di Milano, Via Ponzio 34/3, 20133, Milan, Italy aut NATIONALLICENCE 700 1- Palade Vasile Faculty of Engineering and Computing, Coventry University, Priory Street, Coventry, UK aut NATIONALLICENCE 700 1- Zio Enrico Department of Energy, Politecnico di Milano, Via Ponzio 34/3, 20133, Milan, Italy aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/3(2015-04-01), 525-539 0941-0643 26:3<525 2015 26 521