Diversified Random Forests Using Random Subspaces

Fawagreh, Khaled; Gaber, Mohamed Medhat; Elyan, Eyad

doi:10.1007/978-3-319-10840-7_11

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Random forest implicitly enforces diversity by employing the random subspace algorithm to deduce splitting features from the attribute space for each root node of the individual trees [39]. BRAF boosts the already existing implicit diversity by externally generating more bootstrap samples from the difficult areas dataset, T c .…”

Section: Experimental Setup and Analysismentioning

confidence: 99%

Biased Random Forest For Dealing With the Class Imbalance Problem

Bader-El-Den

Teitei

Perry³

2019

IEEE Trans. Neural Netw. Learning Syst.

106

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The class imbalance issue has been a persistent problem in machine learning that hinders the accurate predictive analysis of data in many real-world applications. The Class imbalance problem exists when the number of instances present in a class (or classes) is significantly fewer than the number of instances belonging to another class (or classes). Sufficiently recognising the minority class during classification is a problem as most algorithms employed to learn from data input are biased towards the majority class. The underlying issue is made more complex with the presence of data difficult factors embedded in such data input. This paper presents a novel and effective ensemble-based method for dealing with the class imbalance problem. This study is motivated by the idea of moving the oversampling from the data level to the algorithm level, instead of increasing the the minority instances in the datasets, the algorithms in this paper aims to âoversample the classification ensembleâ by increasing the number of classifiers that represent the minority class in the ensemble i.e. Random Forest. The proposed Biased Random Forest BRAF algorithm employs the nearest neighbour algorithm to identify the critical areas in a given dataset. The standard random forest is then fed with more random-trees generated based on the critical areas. The results show that the proposed algorithm is very effective in dealing with the class imbalance problem.

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Section: Experimental Setup and Analysismentioning

confidence: 99%

Biased Random Forest For Dealing With the Class Imbalance Problem

Bader-El-Den

Teitei

Perry³

2019

IEEE Trans. Neural Netw. Learning Syst.

106

View full text Add to dashboard Cite

show abstract

“…The subset of features is drawn randomly and this randomization promotes diversification in classification. The authors of [41] further increased the diversity of Random Forests by splitting the training samples into smaller subspaces, and showed improved classification accuracy in their medical datasets over regular Random Forests classification. However, improving the performance of supervised learning through balancing the per-class distribution of training samples can be achieved only if the quality of individual samples exhibits relatively similar condition across and within classes.…”

Section: Introductionmentioning

confidence: 99%

Maximizing the Diversity of Ensemble Random Forests for Tree Genera Classification Using High Density LiDAR Data

Sohn

Remmel

et al. 2016

Remote Sensing

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Recent research into improving the effectiveness of forest inventory management using airborne LiDAR data has focused on developing advanced theories in data analytics. Furthermore, supervised learning as a predictive model for classifying tree genera (and species, where possible) has been gaining popularity in order to minimize this labor-intensive task. However, bottlenecks remain that hinder the immediate adoption of supervised learning methods. With supervised classification, training samples are required for learning the parameters that govern the performance of a classifier, yet the selection of training data is often subjective and the quality of such samples is critically important. For LiDAR scanning in forest environments, the quantification of data quality is somewhat abstract, normally referring to some metric related to the completeness of individual tree crowns; however, this is not an issue that has received much attention in the literature. Intuitively the choice of training samples having varying quality will affect classification accuracy. In this paper a Diversity Index (DI) is proposed that characterizes the diversity of data quality (Q i ) among selected training samples required for constructing a classification model of tree genera. The training sample is diversified in terms of data quality as opposed to the number of samples per class. The diversified training sample allows the classifier to better learn the positive and negative instances and; therefore; has a higher classification accuracy in discriminating the "unknown" class samples from the "known" samples. Our algorithm is implemented within the Random Forests base classifiers with six derived geometric features from LiDAR data. The training sample contains three tree genera (pine; poplar; and maple) and the validation samples contains four labels (pine; poplar; maple; and "unknown"). Classification accuracy improved from 72.8%; when training samples were selected randomly (with stratified sample size); to 93.8%; when samples were selected with additional criteria; and from 88.4% to 93.8% when an ensemble method was used.

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“…Clustering was the main technique used in [8,9] to extreme pruning of random forests, and in [26], replicator dynamics was employed on a diversified random forest with subforests produced by randomised subspaces [27], to evolve subforests by allowing those with better performance to grow and those with lower performance to shrink. The use of replicator dynamics allowed subspaces of features that interact better for accurate classification to have more trees, and those subspaces that have features that are not interacting well for classification, in comparison to other subspaces, to have fewer trees.…”

Section: Clusteringmentioning

confidence: 99%

eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning

Fawagreh

Gaber

2020

BDCC

Self Cite

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To make healthcare available and easily accessible, the Internet of Things (IoT), which paved the way to the construction of smart cities, marked the birth of many smart applications in numerous areas, including healthcare. As a result, smart healthcare applications have been and are being developed to provide, using mobile and electronic technology, higher diagnosis quality of the diseases, better treatment of the patients, and improved quality of lives. Since smart healthcare applications that are mainly concerned with the prediction of healthcare data (like diseases for example) rely on predictive healthcare data analytics, it is imperative for such predictive healthcare data analytics to be as accurate as possible. In this paper, we will exploit supervised machine learning methods in classification and regression to improve the performance of the traditional Random Forest on healthcare datasets, both in terms of accuracy and classification/regression speed, in order to produce an effective and efficient smart healthcare application, which we have termed eGAP. eGAP uses the evolutionary game theoretic approach replicator dynamics to evolve a Random Forest ensemble. Trees of high resemblance in an initial Random Forest are clustered, and then clusters grow and shrink by adding and removing trees using replicator dynamics, according to the predictive accuracy of each subforest represented by a cluster of trees. All clusters have an initial number of trees that is equal to the number of trees in the smallest cluster. Cluster growth is performed using trees that are not initially sampled. The speed and accuracy of the proposed method have been demonstrated by an experimental study on 10 classification and 10 regression medical datasets.

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Diversified Random Forests Using Random Subspaces

Cited by 7 publications

References 17 publications

Biased Random Forest For Dealing With the Class Imbalance Problem

Biased Random Forest For Dealing With the Class Imbalance Problem

Maximizing the Diversity of Ensemble Random Forests for Tree Genera Classification Using High Density LiDAR Data

eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning

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