Boost a Weak Learner to a Strong Learner Using Ensemble System Approach

Vaghela, Vimalkumar B.; Ganatra, Amit; Thakkar, Amit

doi:10.1109/iadcc.2009.4809227

Cited by 14 publications

(6 citation statements)

References 4 publications

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“…The used target detection methods are based on supervised learning to train a model from a data set with both the inputs (images) and the desired results (ROI containing our UAV; Vaghela, Ganatra, & Thakkar, 2009). We have tested YOLO (Redmon & Farhadi, 2018) and SSD (W. Liu et al, 2016) (Section 4.1).…”

Section: Resultsmentioning

confidence: 99%

Two‐stage 3D model‐based UAV pose estimation: A comparison of methods for optimization

Santos

Lobo

Bernardino

2020

Journal of Field Robotics

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Particle Filters (PFs) have been successfully used in three-dimensional (3D) modelbased pose estimation. Typically, these filters depend on the computation of importance weights that use similarity metrics as a proxy to approximate the likelihood function. In this paper, we explore the use of a two-stage 3D model-based approach based on a PF for single-frame pose estimation. First, we use a classifier trained in a synthetic data set for Unmanned Aerial Vehicle (UAV) detection and a pretrained database indexed by bounding boxes properties to obtain an initial rough pose estimate. Second, we employ optimization algorithms to optimize the used similarity metrics and decrease the obtained error. We have tested four different algorithms: (a) Particle Filter Optimization (PFO), (b) Particle Swarm Optimization (PSO), (c) modified PSO, and (d) an approach based on the evolution strategies present in the genetic algorithms named Genetic Algorithm-based Framework (GAbF). To check the quality of the estimate on each iteration, we have tested several similarity metrics (color, edge, and mask-based) based on the UAV Computer-Aided Design (CAD) model. The framework is applied to the outdoor pose estimation of a fixed-wing UAV for autonomous landing in a Fast Patrol Boat (FPB). We extend our previous approach by adopting a better problem formulation, using Deep Neural Networks (DNNs) for UAV detection, making the comparison between the used similarity metrics, comparing pose optimization schemes, and showing new results.The future work will focus on the inclusion of this scheme in a tracking architecture to increase the accuracy of the result between observations.

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Section: Resultsmentioning

confidence: 99%

Two‐stage 3D model‐based UAV pose estimation: A comparison of methods for optimization

Santos

Lobo

Bernardino

2020

Journal of Field Robotics

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“…Unlike RF, LightGBM uses 'boosting' rather than 'bagging' to enhance performance. Boosting is another ensemble method in which classification trees are grown on the residuals from previous trees (Vaghela et al, 2009;Schapire and Freund, 2013). Tree growth and learning rate are controlled by a suite of tunable parameters (Table 3).…”

Section: Model Developmentmentioning

confidence: 99%

Spatial distribution of benthic flora and fauna of coastal placentia bay, an ecologically and biologically significant area of the island of newfoundland, atlantic Canada

Mackin-McLaughlin

Nemani²,

Misiuk³

et al. 2022

Front. Environ. Sci.

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Coastal habitats have the potential to be biodiversity hotspots that provide important ecosystem services, but also hotspots for human development and exploitation. Continued use of coastal ecosystem services requires establishing baselines that capture the present state of the benthos. This study employs habitat mapping to establish a baseline describing the spatial distribution of benthic organisms along the western coast of Placentia Bay, an Ecologically and Biologically Significant Area (EBSA) in Newfoundland, Canada. The influence of seafloor characteristics on the distribution of four dominant epifaunal assemblages and two macrophyte species were modelled using two machine learning techniques: the well-established Random Forest and the newer Light Gradient Boosting Machine. When investigating model performance, the inclusion of fine-scale (<1 m) substrate information from the benthic videos was found to consistently improve model accuracy. Predictive maps developed here suggest that the majority of the surveyed areas consisted of a species-rich epifaunal assemblage dominated by ophiuroids, porifera, and hydrozoans, as well as prominent coverage by Agarum clathratum and non-geniculate crustose coralline algae. These maps establish a baseline that enables future monitoring of Placentia Bay’s coastal ecosystem, helping to conserve the biodiversity and ecosystem services this area provides.

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“…Boosting is a meta-learner that sequentially applies weak base-line learner on a re-weighted training dataset [45]. According to Sun, et al [46], at the end of boosting the meta-learner training phase, a majority vote rule is applied to the generated hypotheses from the weak base-line learner for making its final decision into a final hypothesis.…”

Section: Boostingmentioning

confidence: 99%

Improving the phishing website detection using empirical analysis of Function Tree and its variants

Balogun

Adewole

Raheem

et al. 2021

Heliyon

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The phishing attack is one of the most complex threats that have put internet users and legitimate web resource owners at risk. The recent rise in the number of phishing attacks has instilled distrust in legitimate internet users, making them feel less safe even in the presence of powerful antivirus apps. Reports of a rise in financial damages as a result of phishing website attacks have caused grave concern. Several methods, including blacklists and machine learning-based models, have been proposed to combat phishing website attacks. The blacklist antiphishing method has been faulted for failure to detect new phishing URLs due to its reliance on compiled blacklisted phishing URLs. Many ML methods for detecting phishing websites have been reported with relatively low detection accuracy and high false alarm. Hence, this research proposed a Functional Tree (FT) based metalearning models for detecting phishing websites. That is, this study investigated improving the phishing website detection using empirical analysis of FT and its variants. The proposed models outperformed baseline classifiers, meta-learners and hybrid models that are used for phishing websites detection in existing studies. Besides, the proposed FT based meta-learners are effective for detecting legitimate and phishing websites with accuracy as high as 98.51% and a false positive rate as low as 0.015. Hence, the deployment and adoption of FT and its metalearner variants for phishing website detection and applicable cybersecurity attacks are recommended.

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Boost a Weak Learner to a Strong Learner Using Ensemble System Approach

Cited by 14 publications

References 4 publications

Two‐stage 3D model‐based UAV pose estimation: A comparison of methods for optimization

Two‐stage 3D model‐based UAV pose estimation: A comparison of methods for optimization

Spatial distribution of benthic flora and fauna of coastal placentia bay, an ecologically and biologically significant area of the island of newfoundland, atlantic Canada

Improving the phishing website detection using empirical analysis of Function Tree and its variants

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