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• W2961574599 startingPage "112801" @default.
• W2961574599 abstract "Random forest (RF) is one of the most powerful ensemble methods with high performance when dealing with high dimensional data. In this paper, we present a novel approach, inspired by multi-view theory and by human multi-view approach in object recognition, to increase the performance of RF by increasing the number of trees and limiting the number of levels for each tree in RF. The standard RF algorithm constructs the trees to a maximum possible depth until no further classification is needed, or no further feature is available. In the proposed method, trees of RF are bounded to a certain depth to allow increasing the views. That is why this approach is called “Increasing the Views and Reducing the Depth (IVRD)” approach in RF. Each bounded tree is considered as a local view of the problem and the more local view means the better classification/recognition capability. IVRD approach in RF has been tested on a group of synthetic datasets, a standard dataset, and two datasets of Down Syndrome subjects. Several evaluations have been conducted to determine the effects of the tree depth and the number of trees in a forest on the accuracy of IVRD approach. The results showed by bounding the number of levels in each tree and increasing the number of trees in the forest, the accuracy can be improved, especially in very high dimensional problems." @default.
• W2961574599 created "2019-07-23" @default.
• W2961574599 creator A5038981010 @default.
• W2961574599 creator A5049536848 @default.
• W2961574599 date "2019-12-01" @default.
• W2961574599 modified "2023-09-29" @default.
• W2961574599 title "Increasing the views and reducing the depth in random forest" @default.
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• W2961574599 doi "https://doi.org/10.1016/j.eswa.2019.07.018" @default.
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