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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2022

05.11.2021

Classification Algorithm Using Branches Importance

verfasst von: Youness Manzali, Mohamed Chahhou, Mohammed El Mohajir

Erschienen in: Neural Processing Letters | Ausgabe 2/2022

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Abstract

Ensemble methods have attracted a wide attention, as they are learning algorithms that construct a set of classifiers and then classify new data points by taking a weighted vote of their predictions instead of creating one classifier. Random Forest is one of the most popular and powerful ensemble methods, but it suffers from some drawbacks, such as interpretability and time consumption in the prediction phase. In this paper, we introduce a new algorithm branch classification ’BrClssf’ that classifies observations using branches instead of trees, these branches are extracted from a set of randomized trees. The novelty of the proposed method is that it classifies instances according to the branch’s importance, which is defined by some criteria. This algorithm avoids the drawbacks of ensemble methods while remaining efficient. BrClssf was compared to the state-of-the-art algorithms and the results over 15 databases from the UCI Repository and Kaggle show that the BrClssf algorithm gives good performance.
Vorheriger Artikel Application of Supervised Machine Learning Methods on the Multidimensional Knapsack Problem
Nächster Artikel An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image

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Metadaten
Titel
Classification Algorithm Using Branches Importance
verfasst von
Youness Manzali
Mohamed Chahhou
Mohammed El Mohajir
Publikationsdatum
05.11.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10664-x

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