Modeling Influenced Criteria in Classifiers' Imbalanced Challenges Based on TrSS Bolstered by The Vague Nature of Neutrosophic Theory
Ibrahim El-Henawy · Shrouk El-Amir · Mona Mohamed · Florentin Smarandache
јан 2024. · Infinite Study
Е-књига
16
Страница
family_home
Испуњава услове
info
reportОцене и рецензије нису верификоване Сазнајте више
О овој е-књизи
Because of the advancements in technology, classification learning has become an essential activity in today's environment. Unfortunately, through the classification process, we noticed that the classifiers are unable to deal with the imbalanced data, which indicates there are many more instances (majority instances) in one class than in another. Identifying an appropriate classifier among the various candidates is a time-consuming and complex effort. Improper selection can hinder the classification model's ability to provide the right outcomes. Also, this operation requires preference among a set of alternatives by a set of criteria. Hence, multi-criteria decision-making (MCDM) methodology is the appropriate methodology can deploy in this problem. Accordingly, we applied MCDM and supported it through harnessing neurotrophic theory as motivators in uncertainty circumstances. Single value Neutrosophic sets (SVNSs) are applied as branch of Neutrosophic theory for evaluating and ranks classifiers and allows experts to select the best classifier So, to select the best classifier (alternative), we use MCDM method called Multi- Attributive Ideal-Real Comparative Analysis (MAIRAC) and the criteria weight calculation method called Stepwise Weight Assessment Ratio Analysis (SWARA) where these methods consider single-value neutrosophic sets (SVNSs) to improve and boost these techniques in uncertain scenarios. All these methods are applied after modeling criteria and its sub-criteria through a novel technique is Tree Soft Sets (TrSS). Ultimately, the findings of leveraging these techniques indicated that the hybrid multi-criteria meta-learner (HML)-based classifier is the best classifier compared to the other compared models.
Оцените ову е-књигу
Јавите нам своје мишљење.
Информације о читању
Паметни телефони и таблети
Инсталирајте апликацију Google Play књиге за Android и iPad/iPhone. Аутоматски се синхронизује са налогом и омогућава вам да читате онлајн и офлајн где год да се налазите.
Лаптопови и рачунари
Можете да слушате аудио-књиге купљене на Google Play-у помоћу веб-прегледача на рачунару.
Е-читачи и други уређаји
Да бисте читали на уређајима које користе е-мастило, као што су Kobo е-читачи, треба да преузмете фајл и пренесете га на уређај. Пратите детаљна упутства из центра за помоћ да бисте пренели фајлове у подржане е-читаче.
