Tuning hyperparameters of machine learning algorithms and deep neural networks using metaheuristics: A bioinformatics study on biomedical and biological cases

Basit Öğe Kaydı
dc.authoridFarzad Kiani / 0000-0002-0354-9344
dc.authorscopusidFarzad Kiani / 36662461100
dc.authorwosidFarzad Kiani / O-3363-2013
dc.contributor.authorNematzadeh, S.
dc.contributor.authorKiani, F.
dc.contributor.authorTorkamanian-Afshar, M.
dc.contributor.authorAydin, N.
dc.date.accessioned2022-01-26T11:43:51Z
dc.date.available2022-01-26T11:43:51Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractThe performance of a model in machine learning problems highly depends on the dataset and training algorithms. Choosing the right training algorithm can change the tale of a model. While some algorithms have a great performance in some datasets, they may fall into trouble in other datasets. Moreover, by adjusting hyperparameters of an algorithm, which controls the training processes, the performance can be improved. This study contributes a method to tune hyperparameters of machine learning algorithms using Grey Wolf Optimization (GWO) and Genetic algorithm (GA) metaheuristics. Also, 11 different algorithms including Averaged Perceptron, FastTree, FastForest, Light Gradient Boost Machine (LGBM), Limited memory Broyden Fletcher Goldfarb Shanno algorithm Maximum Entropy (LbfgsMxEnt), Linear Support Vector Machine (LinearSVM), and a Deep Neural Network (DNN) including four architectures are employed on 11 datasets in different biological, biomedical, and nature categories such as molecular interactions, cancer, clinical diagnosis, behavior related predictions, RGB images of human skin, and X-rays images of Covid19 and cardiomegaly patients. Our results show that in all trials, the performance of the training phases is improved. Also, GWO demonstrates a better performance with a p-value of 2.6E-5. Moreover, in most experiment cases of this study, the metaheuristic methods demonstrate better performance and faster convergence than Exhaustive Grid Search (EGS). The proposed method just receives a dataset as an input and suggests the best-explored algorithm with related arguments. So, it is appropriate for datasets with unknown distribution, machine learning algorithms with complex behavior, or users who are not experts in analytical statistics and data science algorithms. © 2022 Elsevier Ltden_US
dc.identifier.citationNematzadeh, S., Kiani, F., Torkamanian-Afshar, M., & Aydin, N. (2022). Tuning hyperparameters of machine learning algorithms and deep neural networks using metaheuristics: A bioinformatics study on biomedical and biological cases. Computational Biology and Chemistry, 97 doi:10.1016/j.compbiolchem.2021.107619en_US
dc.identifier.doi10.1016/j.compbiolchem.2021.107619en_US
dc.identifier.issn1476-9271en_US
dc.identifier.scopus2-s2.0-85122778514en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiolchem.2021.107619
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2420
dc.identifier.volume97en_US
dc.identifier.wosWOS:000793421400004en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorKiani, Farzad
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofComputational Biology and Chemistryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBioinformaticsen_US
dc.subjectHyperparametersen_US
dc.subjectMachine Learningen_US
dc.subjectMetaheuristicsen_US
dc.subjectTuningen_US
dc.subjectDeep Learningen_US
dc.titleTuning hyperparameters of machine learning algorithms and deep neural networks using metaheuristics: A bioinformatics study on biomedical and biological casesen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
252.pdf
Boyut:
13.36 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu
Yazılım Mühendisliği Bölümü Makale Koleksiyonu