Representation learning with deep extreme learning machines for efficient image set classification

Uzair, Muhammad; Ghanem, Bernard; Mian, Ajmal

doi:10.1007/s00521-016-2758-x

Cited by 49 publications

(22 citation statements)

References 43 publications

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“…SVM [26][27][28][29][30] is based on VC theory and structural risk minimization criterion in statistical learning theory, SVM algorithm combines many techniques and methods, such as kernel, sparse solution, loose variables, convex quadratic programming and maximum interval hyperplane, etc. it has advantages in solving problems of small sample, non-linear, high dimension, local minimum, global optimization and generalization performance.…”

Section: Svmmentioning

confidence: 99%

“…Compared with particle swarm optimization, the iterative equation of quantum particle swarm optimization algorithm does not need particle velocity vector, and needs less parameters to be adjusted, so it can be realized more easily. The experimental results on widely used benchmark functions show that the quantum particle swarm optimization algorithm has better performance than the standard particle swarm optimization algorithm [25][26]. For the parameter optimization problem of the classifier parameters, the quantum particle swarm optimization algorithm [40] will be used to optimize the basic parameters (penalty factor, kernel parameter) and the adjustable parameters of the SVM to obtain the optimal combination of parameters.…”

Section: Introductionmentioning

confidence: 99%

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Application of coupled FICA-FCM feature extraction and QPSO-SVM classifier model to predict coal and gas outbursts

Liu¹,

Zhang²,

Li³

et al. 2020

Preprint

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The coal and gas outbursts samples data are affected by all kinds of influencing factors, the accuracy of classification on these sample data is not high, the classification of some samples always have errors, which may be inaccurate annotation. In order to reduce the impact of noise data caused by the labeling errors on classification, this paper proposes a combination of classifier and clustering analysis model, which is used to improve the prediction accuracy of coal and gas outbursts. First, the high-order statistical characteristics of fast independent component analysis(FICA) method are used to extract the features of coal and gas outbursts sample data to obtain the independent nonlinear main metadata; Then, the fuzzy clustering mean(FCM) algorithm is used to cluster the coal and gas outbursts samples. Finally, based on improved clustering analysis results, the support vector machine(SVM) classifier based on quantum particle swarm optimization(QPSO) algorithm is used to compare the classification results with the existing labeled results, and the classification results are corrected to improve the classification accuracy. The results show that the performance of coupling model is significantly better than other exiting classifiers for coal and gas outbursts prediction.

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Section: Svmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of coupled FICA-FCM feature extraction and QPSO-SVM classifier model to predict coal and gas outbursts

Liu¹,

Zhang²,

Li³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The extreme learning machine is a supervised learning algorithm originally for a single hidden layer feedforward neural network [3,21]. But after extensive research in the past few years, it has been modified and updated to work for deep neural networks as well, details can be found here [34][35][36][37]. We use the original form of the ELM, to keep things simple and fast.…”

Section: Layer 2: Extreme Learning Machine Ensemblementioning

confidence: 99%

Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation

Sharma

Giri

Granmo

et al. 2019

EURASIP J. on Info. Security

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Recent advances in intrusion detection systems based on machine learning have indeed outperformed other techniques, but struggle with detecting multiple classes of attacks with high accuracy. We propose a method that works in three stages. First, the ExtraTrees classifier is used to select relevant features for each type of attack individually for each (ELM). Then, an ensemble of ELMs is used to detect each type of attack separately. Finally, the results of all ELMs are combined using a softmax layer to refine the results and increase the accuracy further. The intuition behind our system is that multi-class classification is quite difficult compared to binary classification. So, we divide the multi-class problem into multiple binary classifications. We test our method on the UNSW and KDDcup99 datasets. The results clearly show that our proposed method is able to outperform all the other methods, with a high margin. Our system is able to achieve 98.24% and 99.76% accuracy for multi-class classification on the UNSW and KDDcup99 datasets, respectively. Additionally, we use the weighted extreme learning machine to alleviate the problem of imbalance in classification of attacks, which further boosts performance. Lastly, we implement the ensemble of ELMs in parallel using GPUs to perform intrusion detection in real time.

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“…In terms of algorithms, various machine-learning models, such as Naïve Bayes [15,16], Ensemble [17], or Deep Learning Structure [18] have been used for crime prediction, but Deep Neural Networks (DNN) provided better results in our previous experiments. This study uses DNN because it reflects representation learning and has been used in crosslingual transfer [19], speech recognition [20][21][22][23], image recognition [24][25][26][27], sentiment analysis [28][29][30][31][32], and biomedical [33]. Although the upper bound of the prediction performance still depends on the problem and the data themselves, DNN's auto-feature extraction [34] allows us to use rapid model building without feature processing, thus reducing the application threshold due to feature processing.…”

Section: Related Workmentioning

confidence: 99%

Grid-Based Crime Prediction Using Geographical Features

Lin

Yen

2018

IJGI

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Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design's baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline.

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Representation learning with deep extreme learning machines for efficient image set classification

Cited by 49 publications

References 43 publications

Application of coupled FICA-FCM feature extraction and QPSO-SVM classifier model to predict coal and gas outbursts

Application of coupled FICA-FCM feature extraction and QPSO-SVM classifier model to predict coal and gas outbursts

Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation

Grid-Based Crime Prediction Using Geographical Features

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