Bu öğeden alıntı yapmak, öğeye bağlanmak için bu tanımlayıcıyı kullanınız: http://hdl.handle.net/11452/30049
Başlık: A novel Hybrid Genetic Local Search Algorithm for feature selection and weighting with an application in strategic decision making in innovation management
Yazarlar: Kılıç, Kemal
Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.
0000-0003-4506-9434
Eroğlu, Duygu Yılmaz
AAH-1079-2021
B-3894-2013
56120864000
Anahtar kelimeler: Computer science
Feature subset selection
Feature weighting
Hybrid genetic local search algorithm
Strategic decision support
Benchmarking
Data mining
Decision making
Decision support systems
Feature extraction
Innovation
Learning algorithms
Local search (optimization)
Management science
Manufacture
Nearest neighbor search
Hybrid genetic
Innovation management
Strategic decisions
Innovation management
Classification (of information)
Yayın Tarihi: 5-Nis-2017
Yayıncı: Elsevier
Atıf: Eroğlu, D. Y. ve Kılıç, K. (2017). ''A novel Hybrid Genetic Local Search Algorithm for feature selection and weighting with an application in strategic decision making in innovation management''. Information Sciences, 405, 18-32.
Özet: In some applications, one needs not only to determine the relevant features but also provide a preferential ordering among the set of relevant features by weights. This paper presents a novel Hybrid Genetic Local Search Algorithm (HGA) in combination with the k-nearest neighbor classifier for simultaneous feature subset selection and feature weighting, particularly for medium-sized data sets. The performance of the proposed algorithm is compared with the performance of alternative feature subset selection algorithms and classifiers through experimental analyses in the various benchmark data sets publicly available on the UCI database. The developed HGA is then applied to a data set gathered from 184 manufacturing firms in the context of innovation management. The data set consists of scores of manufacturing firms in terms of various factors that are known to influence the innovation performance of manufacturing firms and referred to as innovation determinants, and their innovation performances. HGA is used to determine the relative significance of the innovation determinants. Our results demonstrated that the developed HGA is capable of eliminating the irrelevant features and successfully assess feature weights. Moreover, our work is an example how data mining can play a role in the context of strategic management decision making.
URI: https://doi.org/10.1016/j.ins.2017.04.009
https://www.sciencedirect.com/science/article/pii/S0020025517306497
1872-6291
http://hdl.handle.net/11452/30049
ISSN: 0020-0255
Koleksiyonlarda Görünür:Scopus
Web of Science

Bu öğenin dosyaları:
Bu öğeyle ilişkili dosya bulunmamaktadır.


DSpace'deki bütün öğeler, aksi belirtilmedikçe, tüm hakları saklı tutulmak şartıyla telif hakkı ile korunmaktadır.