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Research on Optimization of Random Forest Algorithm Based on Spark
2022
Computers Materials & Continua
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This improved random forest algorithm performs feature extraction according to the calculated feature importance to form a feature subspace. ...
However, the random forest algorithm uses a simple random sampling feature selection method when generating feature subspaces which cannot distinguish redundant features, thereby affecting its classification ...
that there are Sub features in the feature subspace, then the strong correlation feature in the feature subspace number NumNS is: Num NS = Sub • S NS (8) Among them, S NS is the proportion of the importance ...
doi:10.32604/cmc.2022.015378
fatcat:yfm4i6t3q5e4pey6sj5ragunva
Unbiased Feature Selection in Learning Random Forests for High-Dimensional Data
2015
The Scientific World Journal
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However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. ...
This feature subset is then partitioned into two subsets. A feature weighting sampling technique is used to sample features from these two subsets for building trees. ...
a random forest uses in-bag samples to produce a kind of importance measure, called an inbag importance score. ...
doi:10.1155/2015/471371
pmid:25879059
pmcid:PMC4387916
fatcat:srvuskevzbchtoiolzw2pkeulq
Online sketching for big data subspace learning
2015
2015 23rd European Signal Processing Conference (EUSIPCO)
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Leveraging the online subspace updates, we introduce a notion of importance score, which is subsequently adapted into a randomization scheme to predict a minimal subset of important features to acquire ...
in the next time instant. ...
For a prescribed maximum sample count , one can then draw random trials from the distribution to collect the important features in the set Ω . ...
doi:10.1109/eusipco.2015.7362837
dblp:conf/eusipco/MardaniG15
fatcat:mt2plztsvfebrajmtaornol2ou
A Novel Random Subspace Method for Online Writeprint Identification
2012
Journal of Computers
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In this paper, we proposed a novel random subspace method by constructing a set of stable classifiers to take advantage of nearly all the discriminative information in the high dimensional feature space ...
random subspace methods. ...
ACKNOWLEDGMENT This work was supported by the National Key Technology R&D Program in the 12th Five-Year Plan (Grant No. 2011BAK08B03, 2011BAK08B05), Program for New Century Excellent Talents in University ...
doi:10.4304/jcp.7.12.2997-3004
fatcat:iccamncnb5hfrol7xeq5gjo64m
Bagging and the Random Subspace Method for Redundant Feature Spaces
[chapter]
2001
Lecture Notes in Computer Science
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In this paper, on the example of the pseudo Fisher linear classifier, we study the effect of the redundancy in the data feature set on the performance of the random subspace method and bagging. b ...
The performance of a single weak classifier can be improved by using combining techniques such as bagging, boosting and the random subspace method. ...
In order to construct good classifiers in random subspaces, it is important that each subspace would contain as much as possible useful information. ...
doi:10.1007/3-540-48219-9_1
fatcat:nbj72qlwgbf4bjojwnq2azxl6y
Semi-supervised Text Categorization by Considering Sufficiency and Diversity
[chapter]
2013
Communications in Computer and Information Science
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Moreover, we further improve the random feature subspace-based bootstrapping with some constraints on the subspace generation to better satisfy the diversity preference. ...
After carefully considering the diversity preference, we modify the traditional bootstrapping algorithm by training the involved classifiers with random feature subspaces instead of the whole feature space ...
Bootstrapping algorithm with random subspace classifiers The size of the feature subset r is an important parameter in this algorithm. ...
doi:10.1007/978-3-642-41644-6_11
fatcat:ovcg5rk6inartgessrv6ccon7q
Weighted random subspace method for high dimensional data classification
2009
Statistics and its Interface
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The aggregating algorithms, e.g. the bagging predictor, the boosting algorithm, the random subspace method, and the Random Forests algorithm, are promising in handling high dimensional data. ...
We have applied the proposed weight assignment procedures to the random subspace method to develop a weighted random subspace method. ...
ACKNOWLEDGEMENTS This work was supported in part from NHLB/NIH contract N01-HV-28186, NIDA/NIH grant P30 DA 018343-01, and NIGMS grant R01 GM 59507.
Received 29 September 2008 ...
doi:10.4310/sii.2009.v2.n2.a5
pmid:21918713
pmcid:PMC3170928
fatcat:a65wx6f3one7xcbtmn6i4gwyey
Super RaSE: Super Random Subspace Ensemble Classification
2021
Journal of Risk and Financial Management
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In this work, we show that Super RaSE avoids the need to choose a base classifier by randomly sampling a collection of classifiers together with the subspace. ...
We propose a new ensemble classification algorithm, named super random subspace ensemble (Super RaSE), to tackle the sparse classification problem. ...
In addition, the increase in sample size leads to the selection of all important features with almost 100% percentage. ...
doi:10.3390/jrfm14120612
fatcat:m5bjw6hihzawnnpj7yw5eosiqa
Stratified sampling for feature subspace selection in random forests for high dimensional data
2013
Pattern Recognition
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Random forest algorithms tend to use a simple random sampling of features in building their decision trees and consequently select many subspaces that contain few, if any, informative features. ...
In this paper we propose a stratified sampling method to select the feature subspaces for random forests with high dimensional data. The key idea is to stratify features into two groups. ...
In Section 2, we review random forests. In Section 3, we present the stratified sampling method for feature subspace selection. ...
doi:10.1016/j.patcog.2012.09.005
fatcat:6qdqqjigqzfsjk6ucj22nigphy
Random Sampling for Subspace Face Recognition
2006
International Journal of Computer Vision
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Instead of pursuing a single optimal subspace, we develop an ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples ...
In addition, we further apply random sampling on parameter selection in order to overcome the difficulty of selecting optimal parameters in our algorithms. ...
Acknowledgements The work described in this paper was fully supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region and a joint grant (N CUHK409/03) from HKSAR ...
doi:10.1007/s11263-006-8098-z
fatcat:kburavsjn5gfddyrytcol7jqce
An Improved Random Forest Classifier for Text Categorization
2012
Journal of Computers
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With the new feature weighting method for subspace sampling and tree selection method, we can effectively reduce subspace size and improve classification performance without increasing error bound. ...
The results have demonstrated that this improved random forests outperformed the popular text classification methods in terms of classification performance. ...
In the future work we will test other feature weighting methods for optimizing the random sampling subspace used in random forest. . ...
doi:10.4304/jcp.7.12.2913-2920
fatcat:p3z4ml3zlfcujfbd6sglgskbei
Classifying Very High-Dimensional Data with Random Forests Built from Small Subspaces
2012
International Journal of Data Warehousing and Mining
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Using a simple random sampling results in informative features not being included in subspaces (Amaratunga, Cabrera, & Lee, 2008) . ...
To build decision trees with improved performance it is important to select subspaces containing more informative features. ...
doi:10.4018/jdwm.2012040103
fatcat:dotaknxqunbujdxoedeb4nl4uu
Multi-feature canonical correlation analysis for face photo-sketch image retrieval
2013
Proceedings of the 21st ACM international conference on Multimedia - MM '13
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The MCCA is an extension and improvement of the canonical correlation analysis (CCA) algorithm using multiple features combined with two different random sampling methods in feature space and sample space ...
Automatic face photo-sketch image retrieval has attracted great attention in recent years due to its important applications in real life. ...
To solve these problems, we apply two popular random sampling methods: random subspace [17] and bagging [18] . ...
doi:10.1145/2502081.2502162
dblp:conf/mm/GongLLQ13
fatcat:565c2ujxxffkjl5or3ourmdyk4
Generating Diverse Ensembles to Counter the Problem of Class Imbalance
[chapter]
2010
Lecture Notes in Computer Science
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In this paper we propose an ensemble framework that combines random subspaces with sampling to overcome the class imbalance problem. ...
In order to combat this, many techniques have been proposed, especially centered around sampling methods. ...
Acknowledgements Work was supported in part by the NSF Grant ECCS-0926170 and the Notebaert Premier Fellowship. ...
doi:10.1007/978-3-642-13672-6_46
fatcat:vc5o5f6bend6pauz7yxjuoiwwy
Random Subspace Learning (RASSEL) with data driven weighting schemes
2018
Mathematics for applications
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We present a novel adaptation of the random subspace learning approach to regression analysis and classification of high dimension low sample size data, in which the use of the individual strength of each ...
The adaptation of random subspace learning presented in this paper differs from random forest in the following ways: (a) instead of using trees as RF does, we use multiple linear regression (MLR) as our ...
Some authors before use, like [23] , in their recent work stratified sampling for feature subspace selection in random forests for high dimensional data, have weighted the trees comprising the random ...
doi:10.13164/ma.2018.02
fatcat:s7o5lziyejc6tk4m2fyptzjx5a
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