Ensemble Deep TimeNet: An Ensemble Learning Approach with Deep Neural Networks for Time Series

Pathak, Sudipta; Cai, Xingyu; Rajasekaran, Sanguthevar

doi:10.1109/iccabs.2018.8541985

Cited by 6 publications

(4 citation statements)

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“…The application of neural network ensembles [35] can improve the effectiveness of the system in comparison with a single network realization. The collective voting for the best answer has already proven to be a valid approach to the problems of Hyperspectral Image Classification [36], co-reference resolution [37], Computer Vision and NLP [38], and healthcare [39].…”

Section: Introductionmentioning

confidence: 99%

Implementation of an Artificial Intelligence Approach to GPR Systems for Landmine Detection

et al. 2022

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Artificial Neural Network (ANN) approaches are applied to detect and determine the object class using a special set of the UltraWideBand (UWB) pulse Ground Penetrating Radar (GPR) sounding results. It used the results of GPR sounding with the antenna system, consisting of one radiator and four receiving antennas located around the transmitting antenna. The presence of four receiving antennas and, accordingly, the signals received from four spatially separated positions of the antennas provide a collection of signals received after reflection from an object at different angles and, due to this, to determine the location of the object in a coordinate system, connected to the antenna. We considered the sums and differences of signals received by two of the four antennas in six possible combinations: (1 and 2, 1 and 3, 2 and 3, 1 and 4, etc.). These combinations were then stacked sequentially one by one into one long signal. Synthetic signals constructed in such a way contain many more notable differences and specific information about the class to which the object belongs as well as the location of the searched object compared to the signals obtained by an antenna system with just one radiating and one receiving antenna. It therefore increases the accuracy in determining the object’s coordinates and its classification. The pulse radiation, propagation, and scattering are numerically simulated by the finite difference time domain (FDTD) method. Results from the experiment on mine detection are used to examine ANN too. The set of signals from different objects having different distances from the GPR was used as a training and testing dataset for ANN. The training aims to recognize and classify the detected object as a landmine or other object and to determine its location. The influence of Gaussian noise added to the signals on noise immunity of ANN was investigated. The recognition results obtained by using an ANN ensemble are presented. The ensemble consists of fully connected and recurrent neural networks, gated recurrent units, and a long-short term memory network. The results of the recognition by all ANNs are processed by a meta network to provide a better quality of underground object classification.

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Section: Introductionmentioning

confidence: 99%

Implementation of an Artificial Intelligence Approach to GPR Systems for Landmine Detection

et al. 2022

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“…Ensemble learning (Breiman, 1996;Galar et al, 2012;Huynh et al, 2016) can combine different DCNN models to obtain better performance than any individual DCNN model using proper strategies. It has been well studied in remote sensing image processing and other aspects (Chen et al, 2019;Hurt et al, 2019;;Pathak et al, 2018;Xia et al, 2018) and proven to improve model performance. However, the research on the automatic recognition of empty camera trap images based on ensemble learning has just begun (Norouzzadeh et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

An automatic method for removing empty camera trap images using ensemble learning

Yang

Tan

Huang

et al. 2021

Ecology and Evolution

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Camera traps often produce massive images, and empty images that do not contain animals are usually overwhelming. Deep learning is a machine‐learning algorithm and widely used to identify empty camera trap images automatically. Existing methods with high accuracy are based on millions of training samples (images) and require a lot of time and personnel costs to label the training samples manually. Reducing the number of training samples can save the cost of manually labeling images. However, the deep learning models based on a small dataset produce a large omission error of animal images that many animal images tend to be identified as empty images, which may lead to loss of the opportunities of discovering and observing species. Therefore, it is still a challenge to build the DCNN model with small errors on a small dataset. Using deep convolutional neural networks and a small‐size dataset, we proposed an ensemble learning approach based on conservative strategies to identify and remove empty images automatically. Furthermore, we proposed three automatic identifying schemes of empty images for users who accept different omission errors of animal images. Our experimental results showed that these three schemes automatically identified and removed 50.78%, 58.48%, and 77.51% of the empty images in the dataset when the omission errors were 0.70%, 1.13%, and 2.54%, respectively. The analysis showed that using our scheme to automatically identify empty images did not omit species information. It only slightly changed the frequency of species occurrence. When only a small dataset was available, our approach provided an alternative to users to automatically identify and remove empty images, which can significantly reduce the time and personnel costs required to manually remove empty images. The cost savings were comparable to the percentage of empty images removed by models.

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“…Nowadays, multiple researchers and practitioners have well understood the benefit of ensembling DNNs. For example, in cyberattack detection [19], time series classification [43], medical image analysis [7], semi-supervision [50] and unbalanced text classification [49]. Further, several winners and top performers on challenges routinely use ensembles to improve accuracy.…”

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confidence: 99%

A Deep Neural Networks ensemble workflow from hyperparameter search to inference leveraging GPU clusters

Pochelu

Petiton

Conche

2022

International Conference on High Performance Computing in Asia-Pacific Region

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Automated Machine Learning with ensembling (or AutoML with ensembling) seeks to automatically build ensembles of Deep Neural Networks (DNNs) to achieve qualitative predictions. Ensemble of DNNs are well known to avoid over-fitting but they are memory and time consuming approaches. Therefore, an ideal AutoML would produce in one single run time different ensembles regarding accuracy and inference speed. While previous works on AutoML focus to search for the best model to maximize its generalization ability, we rather propose a new AutoML to build a larger library of accurate and diverse individual models to then construct ensembles. First, our extensive benchmarks show asynchronous Hyperband is an efficient and robust way to build a large number of diverse models to combine them. Then, a new ensemble selection method based on a multi-objective greedy algorithm is proposed to generate accurate ensembles by controlling their computing cost. Finally, we propose a novel algorithm to optimize the inference of the DNNs ensemble in a GPU cluster based on allocation optimization. The produced AutoML with ensemble method shows robust results on two datasets using efficiently GPU clusters during both the training phase and the inference phase. CCS CONCEPTS• Computing methodologies → Ensemble methods; Search methodologies; Massively parallel algorithms.

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Ensemble Deep TimeNet: An Ensemble Learning Approach with Deep Neural Networks for Time Series

Cited by 6 publications

References 0 publications

Implementation of an Artificial Intelligence Approach to GPR Systems for Landmine Detection

Implementation of an Artificial Intelligence Approach to GPR Systems for Landmine Detection

An automatic method for removing empty camera trap images using ensemble learning

A Deep Neural Networks ensemble workflow from hyperparameter search to inference leveraging GPU clusters

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