عرض بسيط للتسجيلة

المؤلفGanaie, M. A.
المؤلفTanveer, M.
المؤلفSuganthan, P. N.
المؤلفSnasel, V.
تاريخ الإتاحة2023-02-13T07:26:14Z
تاريخ النشر2022-09-01
اسم المنشورNeural Networks
المعرّفhttp://dx.doi.org/10.1016/j.neunet.2022.06.012
الاقتباسGanaie, M. A., Tanveer, M., Suganthan, P. N., & Snásel, V. (2022). Oblique and rotation double random forest. Neural Networks, 153, 496-517.‏
الرقم المعياري الدولي للكتاب08936080
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85133677446&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/40004
الملخصRandom Forest is an ensemble of decision trees based on the bagging and random subspace concepts. As suggested by Breiman, the strength of unstable learners and the diversity among them are the ensemble models’ core strength. In this paper, we propose two approaches known as oblique and rotation double random forests. In the first approach, we propose rotation based double random forest. In rotation based double random forests, transformation or rotation of the feature space is generated at each node. At each node different random feature subspace is chosen for evaluation, hence the transformation at each node is different. Different transformations result in better diversity among the base learners and hence, better generalization performance. With the double random forest as base learner, the data at each node is transformed via two different transformations namely, principal component analysis and linear discriminant analysis. In the second approach, we propose oblique double random forest. Decision trees in random forest and double random forest are univariate, and this results in the generation of axis parallel split which fails to capture the geometric structure of the data. Also, the standard random forest may not grow sufficiently large decision trees resulting in suboptimal performance. To capture the geometric properties and to grow the decision trees of sufficient depth, we propose oblique double random forest. The oblique double random forest models are multivariate decision trees. At each non-leaf node, multisurface proximal support vector machine generates the optimal plane for better generalization performance. Also, different regularization techniques (Tikhonov regularization, axis-parallel split regularization, Null space regularization) are employed for tackling the small sample size problems in the decision trees of oblique double random forest. The proposed ensembles of decision trees produce trees with bigger size compared to the standard ensembles of decision trees as bagging is used at each non-leaf node which results in improved performance. The evaluation of the baseline models and the proposed oblique and rotation double random forest models is performed on benchmark 121 UCI datasets and real-world fisheries datasets. Both statistical analysis and the experimental results demonstrate the efficacy of the proposed oblique and rotation double random forest models compared to the baseline models on the benchmark datasets.
راعي المشروعThis work is supported by Science and Engineering Research Board (SERB), Government of India under Ramanujan Fellowship Scheme, Grant No. SB/S2/RJN-001/2016 , and Department of Science and Technology under Interdisciplinary Cyber Physical Systems (ICPS) Scheme grant no. DST/ICPS/CPS-Individual/2018/276 . We gratefully acknowledge the Indian Institute of Technology Indore for providing facilities and support.
اللغةen
الناشرElsevier Ltd
الموضوعBootstrap
classification
Decision tree
Double random forest
Ensemble learning
Oblique random forest
العنوانOblique and rotation double random forest
النوعArticle
الصفحات496-517
رقم المجلد153
dc.accessType Open Access


الملفات في هذه التسجيلة

Thumbnail

هذه التسجيلة تظهر في المجموعات التالية

عرض بسيط للتسجيلة