Ensemble of Elman neural networks and support vector machines for reverse engineering of gene regulatory networks

Ao, Sio-Iong; Palade, Vasile

doi:10.1016/j.asoc.2010.05.014

Cited by 29 publications

(11 citation statements)

References 41 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5(B). The module 18 mainly includes three parts: quantum clone, OAM sorting setup and control-phase (CPhase) gate. In the quantum clone, the input photon with polarization and OAM can be cloned and two same photons are output.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Entanglement-based quantum deep learning

Yang

Zhang

2020

New J. Phys.

View full text Add to dashboard Cite

Classical deep learning algorithms have aroused great interest in both academia and industry for their utility in image recognition, language translation, decision-making problems and more. In this work, we have provided a quantum deep learning scheme based on multi-qubit entanglement states, including computation and training of neural network in full quantum process. In the course of training, efficient calculation of the distance between unknown unit vector and known unit vector has been realized by proper measurement based on the Greenberger-Horne-Zeilinger entanglement states.An exponential speedup over classical algorithms has been demonstrated. In the process of computation, quantum scheme corresponding to multi-layer feedforward neural network has been provided. We have shown the utility of our scheme using Iris dataset. The extensibility of the present scheme to different types of model has also been analyzed

show abstract

Section: Discussionmentioning

confidence: 99%

“…Elman and Hopfield neural networks can be applied to the task of reconstruction as a memory system [9,18]. It is noteworthy that multi-layer feedforward neural network (MFNN) is a basic model.…”

Section: Introductionmentioning

confidence: 99%

Entanglement-based quantum deep learning

Yang

Zhang

2020

New J. Phys.

View full text Add to dashboard Cite

show abstract

“…The state units of the Elman network can memorize all the feed inputs such that the outputs of the network depend upon the current input as well as the previous inputs. The state layer of the Elman network makes it different from the multilayer perceptron neural network [12].…”

Section: Elman Neural Networkmentioning

confidence: 99%

Fault Diagnosis and Classification in Urban Rail Vehicle Auxiliary Inverter Based on Wavelet Packet and Elman Neural Network

Yao¹,

Jia²,

Ji³

et al. 2013

JESTR

View full text Add to dashboard Cite

In this paper we present a novel method in fault recognition and classification in urban rail vehicle auxiliary inverter based on wavelet packet and Elman neural network. First, the original fault voltage signals are decomposed by wavelet packet. Next, an automatic feature extraction algorithm is constructed. Finally, those wavelet packet energy eigenvectors are used as Elman neural network input parameters to realize intelligent fault diagnosis. The result shows that the Elman neural network is better than BP neural network, it is effective to distinguish the state of the urban rail vehicle auxiliary inverter.

show abstract

“…He used a genetic programming technique to produce rules from ensembles of 20 neural networks. Ao and Palade extracted rules from ensembles of Elman networks and SVMs by means of a pedagogical approach to predict gene expression in microarray data [24]. More recently Hara and Hayashi proposed the two-MLP ensembles by using the "Recursive-Rule eXtraction" (Re-RX) algorithm [25] for data with mixed attributes [26].…”

Section: Rule Extraction From Neural Network Ensemblesmentioning

confidence: 99%

A Comparison Study on Rule Extraction from Neural Network Ensembles, Boosted Shallow Trees, and SVMs

Bologna

Hayashi

2018

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

One way to make the knowledge stored in an artificial neural network more intelligible is to extract symbolic rules. However, producing rules from Multilayer Perceptrons (MLPs) is an NP-hard problem. Many techniques have been introduced to generate rules from single neural networks, but very few were proposed for ensembles. Moreover, experiments were rarely assessed by 10-fold cross-validation trials. In this work, based on the Discretized Interpretable Multilayer Perceptron (DIMLP), experiments were performed on 10 repetitions of stratified 10-fold cross-validation trials over 25 binary classification problems. The DIMLP architecture allowed us to produce rules from DIMLP ensembles, boosted shallow trees (BSTs), and Support Vector Machines (SVM). The complexity of rulesets was measured with the average number of generated rules and average number of antecedents per rule. From the 25 used classification problems, the most complex rulesets were generated from BSTs trained by "gentle boosting" and "real boosting." Moreover, we clearly observed that the less complex the rules were, the better their fidelity was. In fact, rules generated from decision stumps trained by modest boosting were, for almost all the 25 datasets, the simplest with the highest fidelity. Finally, in terms of average predictive accuracy and average ruleset complexity, the comparison of some of our results to those reported in the literature proved to be competitive.

show abstract

Ensemble of Elman neural networks and support vector machines for reverse engineering of gene regulatory networks

Cited by 29 publications

References 41 publications

Entanglement-based quantum deep learning

Entanglement-based quantum deep learning

Fault Diagnosis and Classification in Urban Rail Vehicle Auxiliary Inverter Based on Wavelet Packet and Elman Neural Network

A Comparison Study on Rule Extraction from Neural Network Ensembles, Boosted Shallow Trees, and SVMs

Contact Info

Product

Resources

About