A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification

Deng, Yue; Ren, Zhiquan; Kong, Yaguang; Bao, Feng; Dai, Qionghai

doi:10.1109/tfuzz.2016.2574915

Cited by 302 publications

(126 citation statements)

References 25 publications

Supporting

Mentioning

115

Contrasting

Order By: Relevance

“…Currently, only basic CNNs and GANs have been employed for background subtraction. Thus, future directions may investigate the adequacy and the use of pyramidal deep CNNs [191], deep belief neural networks, deep restricted kernel neural networks [183], probabilistic neural networks [58], deep fuzzy neural networks [46,54] and fully memristive neural networks [33,52,71,102,103,223] in the case of static camera as well as moving camera [133].…”

Section: Resultsmentioning

confidence: 99%

Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

et al. 2019

View full text Add to dashboard Cite

Conventional neural networks show a powerful framework for background subtraction in video acquired by static cameras. Indeed, the well-known SOBS method and its variants based on neural networks were the leader methods on the largescale CDnet 2012 dataset during a long time. Recently, convolutional neural networks which belong to deep learning methods were employed with success for background initialization, foreground detection and deep learned features. Currently, the top current background subtraction methods in CDnet 2014 are based on deep neural networks with a large gap of performance in comparison on the conventional unsupervised approaches based on multi-features or multi-cues strategies. Furthermore, a huge amount of papers was published since 2016 when Braham and Van Droogenbroeck published their first work on CNN applied to background subtraction providing a regular gain of performance. In this context, we provide the first review of deep neural network concepts in background subtraction for novices and experts in order to analyze this success and to provide further directions. For this, we first surveyed the methods used background initialization, background subtraction and deep learned features. Then, we discuss the adequacy of deep neural networks for background subtraction. Finally, experimental results are presented on the CDnet 2014 dataset.

show abstract

Section: Resultsmentioning

confidence: 99%

Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

et al. 2019

View full text Add to dashboard Cite

show abstract

“…During classification (lines 11-12) the function classification is used (lines [24][25][26][27][28][29][30][31][32][33][34][35]. The class for the test instances given as the first parameter (T estData) is predicted.…”

Section: Methods Descriptionmentioning

confidence: 99%

AEkNN: An AutoEncoder kNN–Based Classifier With Built-in Dimensionality Reduction

Pulgar¹,

Charte²,

Rivera³

et al. 2019

IJCIS

View full text Add to dashboard Cite

High dimensionality, i.e. data having a large number of variables, tends to be a challenge for most machine learning tasks, including classification. A classifier usually builds a model representing how a set of inputs explain the outputs. The larger is the set of inputs and/or outputs, the more complex would be that model. There is a family of classification algorithms, known as lazy learning methods, which does not build a model. One of the best known members of this family is the kNN algorithm. Its strategy relies on searching a set of nearest neighbors, using the input variables as position vectors and computing distances among them. These distances loss significance in high-dimensional spaces. Therefore kNN, as many other classifiers, tends to worse its performance as the number of input variables grows.In this work AEkNN, a new kNN-based algorithm with built-in dimensionality reduction, is presented. Aiming to obtain a new representation of the data, having a lower dimensionality but with more informational features, AEkNN internally uses autoencoders. From this new feature vectors the computed distances should be more significant, thus providing a way to choose better neighbors. A experimental evaluation of the new proposal is conducted, analyzing several configurations and comparing them against the classical kNN algorithm. The obtained conclusions demonstrate that AEkNN offers better results in predictive and runtime performance.

show abstract

“…Combining fuzzy theory and neural networks can improve complex data representation with probability distribution over cross-layer units [11]. Even though they have been widely applied in control systems [11] and portfolio management [12], no existing work ap-plies fuzzy neural networks in demand prediction.…”

Section: Introductionmentioning

confidence: 99%

A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction

Liang¹,

Wang²,

Min³

et al. 2019

Proceedings of the 2019 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

In spite of its importance, passenger demand prediction is a highly challenging problem, because the demand is simultaneously influenced by the complex interactions among many spatial and temporal factors and other external factors such as weather. To address this problem, we propose a Spatio-TEmporal Fuzzy neural Network (STEF-Net) to accurately predict passenger demands incorporating the complex interactions of all known important factors. We design an end-toend learning framework with different neural networks modeling different factors. Specifically, we propose to capture spatio-temporal feature interactions via a convolutional long short-term memory network and model external factors via a fuzzy neural network that handles data uncertainty significantly better than deterministic methods. To keep the temporal relations when fusing two networks and emphasize discriminative spatio-temporal feature interactions, we employ a novel feature fusion method with a convolution operation and an attention layer. As far as we know, our work is the first to fuse a deep recurrent neural network and a fuzzy neural network to model complex spatial-temporal feature interactions with additional uncertain input features for predictive learning. Experiments on a large-scale realworld dataset show that our model achieves more than 10% improvement over the state-of-the-art approaches. *

show abstract

A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification

Cited by 302 publications

References 25 publications

Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

AEkNN: An AutoEncoder kNN–Based Classifier With Built-in Dimensionality Reduction

A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction

Contact Info

Product

Resources

About