Combining Bootstrapping Samples, Random Subspaces and Random Forests to Build Classifiers

Daho, Mostafa El Habib; Chikh, Mohammed Amine

doi:10.1166/jmihi.2015.1423

Cited by 27 publications

(14 citation statements)

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“…Random decision forest is similar to the bootstrapping algorithm with decision tree (CART) model. Random decision forest tries to build k different decision trees by picking a random subset S of training samples [ 31 ]. It generates the full Iterative Dichotomiser 3 (ID3) [ 32 ] trees with no pruning.…”

Section: Methodsmentioning

confidence: 99%

Breast Tumor Classification Using an Ensemble Machine Learning Method

2020

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Breast cancer is the most common cause of death for women worldwide. Thus, the ability of artificial intelligence systems to detect possible breast cancer is very important. In this paper, an ensemble classification mechanism is proposed based on a majority voting mechanism. First, the performance of different state-of-the-art machine learning classification algorithms were evaluated for the Wisconsin Breast Cancer Dataset (WBCD). The three best classifiers were then selected based on their F3 score. F3 score is used to emphasize the importance of false negatives (recall) in breast cancer classification. Then, these three classifiers, simple logistic regression learning, support vector machine learning with stochastic gradient descent optimization and multilayer perceptron network, are used for ensemble classification using a voting mechanism. We also evaluated the performance of hard and soft voting mechanism. For hard voting, majority-based voting mechanism was used and for soft voting we used average of probabilities, product of probabilities, maximum of probabilities and minimum of probabilities-based voting methods. The hard voting (majority-based voting) mechanism shows better performance with 99.42%, as compared to the state-of-the-art algorithm for WBCD.

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Section: Methodsmentioning

confidence: 99%

Breast Tumor Classification Using an Ensemble Machine Learning Method

2020

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“…A random subspace-based method was applied to obtain 1,000 descriptor subsets of 200 potential independent variables each. In the random subspace approach, the molecular descriptors are randomly sampled, and each model is trained on one subset of the feature space (Yu et al, 2012; El Habib Daho and Chikh, 2015); as a result, individual models do not over-focus on features that display high explanatory power in the training set.…”

Section: Methodsmentioning

confidence: 99%

“…Classifier ensembles are known to provide better generalization and accuracy than single model classifiers (El Habib Daho and Chikh, 2015; Carbonneau et al, 2016; Min, 2016). Here, we have used two retrospective virtual screening campaigns to assess the performance of individual classifiers and classifier ensembles.…”

Section: Methodsmentioning

confidence: 99%

In silico Guided Drug Repurposing: Discovery of New Competitive and Non-competitive Inhibitors of Falcipain-2

et al. 2019

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Malaria is among the leading causes of death worldwide. The emergence of Plasmodium falciparum resistant strains with reduced sensitivity to the first line combination therapy and suboptimal responses to insecticides used for Anopheles vector management have led to renewed interest in novel therapeutic options. Here, we report the development and validation of an ensemble of ligand-based computational models capable of identifying falcipain-2 inhibitors, and their subsequent application in the virtual screening of DrugBank and Sweetlead libraries. Among four hits submitted to enzymatic assays, two (odanacatib, an abandoned investigational treatment for osteoporosis and bone metastasis, and the antibiotic methacycline) confirmed inhibitory effects on falcipain-2, with Ki of 98.2 nM and 84.4 μM. Interestingly, Methacycline proved to be a non-competitive inhibitor (α = 1.42) of falcipain-2. The effects of both hits on falcipain-2 hemoglobinase activity and on the development of P. falciparum were also studied.

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“…Unlike the selection measure, choice of pruning methods is a more crucial factor that directly affects performance of tree-based classifiers [43]. Previous studies have shown that generalization error converges as tree number increases even without pruning the tree [44]. This is because a tree can grow to its maximum depth using a combination of features each time it has a new training data.…”

Section: Classification Algorithmmentioning

confidence: 99%

Exploration of Interventionists’ Technical Manipulation Skills for Robot-Assisted Intravascular PCI Catheterization

Omisore

Duan

et al. 2020

IEEE Access

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Despite the great proven advantages of imaging techniques in percutaneous coronary interventions (PCIs), the recent rapid increase in the number of PCI procedures exposes both surgical experts and patients to more radiation and orthopedic hazards in the intervention room. While patients are minimally exposed, many appearance of interventionists subject them to frequent exposure of the operational hazards. Despite promoting the use of robot-assisted intravascular PCI in the Cath-Labs, cognitive and technical skills of interventionists' are yet to be explored towards reducing procedure time and minimal exposure of surgeon to operational hazards. In this study, a random forest classification framework is developed for proper identification of technical manipulation skills of surgeons along with underlying motion patterns of the flexible intravascular tools (viz. guidewire and catheter) during PCI catheterization. For this purpose, analysis of interventionists' muscular activities and related hand motion were decoded from physiological signals recorded with sensors. Surface electromyography, electromagnetic, and tactile force signals were acquired concurrently from seven interventionists during specified guidewire movement viz. pull, push, rotate-pull (clockwise rotation with pull), rotate-push (counterclockwise rotation with push), CR (clockwise rotation), and CCR (counterclockwise rotation). While relations were established between force and muscle activities in the surgeon groups using Spearman's Rank-Order statistical method, Wilcoxon test and Kruskal-Wallis one-way ANOVA test were employed to identify intra-group and inter-group differences. From the experimental results obtained, guidewire delivery patterns exhibit stable characteristics with overall classification accuracies of 94.11% based on nineteen features subset from muscle activities and hand motion, followed by 88.01% based on twelve features subset from muscle activities, 71.97% based on seven features subset from hand motion, 84.56% based on ten features subset from part muscle activities and hand motion. Thus, this study shows existence of significant correlation (p < 0.05) between force and muscle activity during intravascular catheterization for PCIs, while comparably high tactile force values were experience in vascular model with plaque or stenosis.

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Combining Bootstrapping Samples, Random Subspaces and Random Forests to Build Classifiers

Cited by 27 publications

References 0 publications

Breast Tumor Classification Using an Ensemble Machine Learning Method

Breast Tumor Classification Using an Ensemble Machine Learning Method

In silico Guided Drug Repurposing: Discovery of New Competitive and Non-competitive Inhibitors of Falcipain-2

Exploration of Interventionists’ Technical Manipulation Skills for Robot-Assisted Intravascular PCI Catheterization

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