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Time-Evolving Relational Classification and Ensemble Methods
[chapter]
2012
Lecture Notes in Computer Science
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Relational networks often evolve over time by the addition, deletion, and changing of links, nodes, and attributes. ...
We propose a novel framework for discovering temporal-relational representations for classification. ...
The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements either expressed or implied, of the NSF ...
doi:10.1007/978-3-642-30217-6_1
fatcat:ngbev72235grrkofuhb42vzoiu
Representations and Ensemble Methods for Dynamic Relational Classification
[article]
2011
arXiv
pre-print
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Temporal networks are ubiquitous and evolve over time by the addition, deletion, and changing of links, nodes, and attributes. ...
The results demonstrate the capability and necessity of the temporal-relational representations for classification, ensembles, and for mining temporal datasets. ...
TEMPORAL-RELATIONAL CLASSIFICATION FRAMEWORK The temporal-relational classification framework is defined with respect to the possible transformations of links, attributes, or nodes (as a function of time ...
arXiv:1111.5312v1
fatcat:ayyvsybdzfhqbdecobsz2uhoii
Ensemble of Ada Booster with SVM Classifier for Anomaly Intrusion Detection in Wireless Ad Hoc Network
2017
Indian Journal of Science and Technology
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After that, an ensemble of Ada booster with SVM (EAB-SVM) classifier is used for classifying the intrusion through updating the weight of samples. ...
Objectives: An ensemble of Ada Booster with SVM (EAB-SVM) classifier technique is proposed for detecting the network intrusions and monitoring the activities of the node as well as classifying it as either ...
Introduction A wireless ad-hoc network comprises group of nodes linked with each other through wireless links without centralized communication. ...
doi:10.17485/ijst/2017/v10i23/114036
fatcat:vfqjwhtibzc73hky7zqy6eaxje
Weighting and Pruning based Ensemble Deep Random Vector Functional Link Network for Tabular Data Classification
[article]
2022
arXiv
pre-print
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Subsequently, the combination of weighting and pruning, called Weighting and Pruning based Ensemble Deep Random Vector Functional Link Network (WPedRVFL), is proposed. ...
Then we propose novel variants of Ensemble Deep Random Vector Functional Link (edRVFL). ...
Ensemble Deep Random Vector Functional Link Network (WPe-dRVFL) is a combination of the above two models. ...
arXiv:2201.05809v2
fatcat:n2h6n23ugzctdpisezzlfjwiii
Random Vector Functional Link Neural Network based Ensemble Deep Learning
[article]
2019
arXiv
pre-print
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In particular, inspired by the principles of Random Vector Functional Link (RVFL) network, we present a deep RVFL network (dRVFL) with stacked layers. ...
We also propose an ensemble deep network (edRVFL) that can be regarded as a marriage of ensemble learning with deep learning. ...
Ensemble Deep Random Vector Functional Link Network The framework of the ensemble deep RVFL network (edRVFL) is shown in Fig. 3 . ...
arXiv:1907.00350v1
fatcat:7kni6ktodzamvcxtafq2aaiqhm
Neural networks for classification: a survey
2000
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
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Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. ...
This paper summarizes the some of the most important developments in neural network classification research. ...
Weight elimination and node pruning are techniques often used to remove unnecessary linking weights or input nodes during the network training. ...
doi:10.1109/5326.897072
fatcat:q4qnyqyr3vfk7awkcunglm7xji
Link Discovery through Iterative Link Classification: Towards a Real-Time Analysis of Graph Evolution
2018
Zenodo
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We also propose an iterative link classification method, which updates the network using only predicted links with a high confidence level at each iteration. ...
In a real-world application that deals with time-varying networks, it is important to understand predictive models in a time-varying context. ...
Table 4-5 The link prediction performance reported by the incremental SVM model using the ensemble method and the linear decay weight function 10 . ...
doi:10.5281/zenodo.7141842
fatcat:p4nhguhpsvd6zpxjado4tztdfu
Video Representation Learning and Latent Concept Mining for Large-scale Multi-label Video Classification
[article]
2017
arXiv
pre-print
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3) Learning with temporal segments and weighted multi-model ensemble. ...
We conduct experiments to validate and analyze the contribution of our models. ...
Ensemble We use leave-one-out method [14] to determine the fusion weights of individual models. ...
arXiv:1707.01408v3
fatcat:xyc4rtvrz5e7pjg5luxuux7ddi
Divide and conquer
2018
Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '18
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Neuroevolution is a powerful and general technique for evolving the structure and weights of artificial neural networks. ...
These ensemble models exhibit reduced variance and increasingly superior accuracy as the number of classes increases. ...
Networks are tuned via mutations that change the weights of existing links. ...
doi:10.1145/3205455.3205476
dblp:conf/gecco/McDonnellABCCGM18
fatcat:d5vrinx6mbg3noubbyyzz6cyoi
A Modified Functional Link Neural Network for Data Classification
[chapter]
2017
Series in BioEngineering
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The rFLNN2 model merges optimisation of both network structure and network weights into one search problem, in order to generate a parsimonious FLNN model with high classification capabilities. ...
In artificial neural network (ANN) research, the functional link neural network (FLNN) is a well-known alternative to the standard feedforward ANNs such as the Multilayer Perceptron network. ...
derivatives; -Updating of the weights of all the links in the network. ...
doi:10.1007/978-981-10-3957-7_13
fatcat:lfjfchicyfeqvlprvrqpp4op5e
Improve the Accuracy of Link Predictions on Sparse Networks based on Similarity Measures and Multiple Ensemble Learning
2020
Journal of Information Hiding and Multimedia Signal Processing
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The experiments conducted on the social networks show that multiple ensemble learning models provide higher predictive efficiency than the existed ensemble learning models and basic classification algorithms ...
To do this, we analyzed the properties of the social networks, thereby building a similarity measure with node-based approach and proposing the application of a multiple ensemble learning algorithm on ...
link weight will be low. ...
dblp:journals/jihmsp/Kim20
fatcat:7prbxj4cyfhvnhkdc6jpuqutsi
A Novel Functional Link Network Stacking Ensemble with Fractal Features for Multichannel Fall Detection
2020
Cognitive Computation
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This paper proposes a novel Random Vector Functional Link (RVFL) stacking ensemble classifier with fractal features for classification of falls. ...
The proposed features and the stacking ensemble provide the highest classification accuracy of 95.71% compared with other machine learning techniques, such as Random Forest (RF), Artificial Neural Network ...
Random Vector Functional Link (RVFL) neural networks introduced by Pao et al. in [42] utilise randomness for a subset of weights and biases between the input and a single hidden layer, which are kept ...
doi:10.1007/s12559-020-09749-x
fatcat:wbisf2kkkvannj35elhk5vw6wa
A Heterogeneous Machine Learning Ensemble Framework for Malicious Webpage Detection
2022
Applied Sciences
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To address these limitations, we propose an ensemble approach using different machine learning models. ...
In this study, repetitive tasks are automated, improving the performance of different machine learning models. ...
Conflicts of Interest: The authors declare no conflict of interest. Appl. Sci. 2022, 12, 12070 ...
doi:10.3390/app122312070
fatcat:4lv5i5gx4fhxfo4dyxictpt2s4
Classifier Ensembles Using Structural Features For Spammer Detection In Online Social Networks
2015
Foundations of Computing and Decision Sciences
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Further, the evaluation of ensemble classifiers for detection of spammers over social networking behavior-based features is still in its infancy. ...
In this paper, we present an ensemble learning method for online social network security by evaluating the performance of some basic ensemble classifiers over novel community-based social networking features ...
function, (iii) Weighted sum of the hypotheses within an ensemble may extend the space of representable hypotheses to allow a more accurate representation. ...
doi:10.1515/fcds-2015-0006
fatcat:wlisrgrmzvcktch4vc3eky2cym
Spammer Classification Using Ensemble Methods over Structural Social Network Features
2014
2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)
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In this paper, we evaluate the performance of some ensemble learning methods using community-based structural features extracted from an interaction network for the task of spammer detection in online ...
The overwhelming growth and popularity of online social networks is also facing the issues of spamming, which mainly leads to uncontrolled dissemination of malware/viruses, promotional ads, phishing, ...
ACKNOWLEDGEMENT The authors acknowledge the support provided by the King Abdulaziz City for Science and Technology (KACST), Kingdom of Saudi Arabia under the NPST project number 11-INF1594-02. ...
doi:10.1109/wi-iat.2014.133
dblp:conf/webi/BhatAM14
fatcat:dml4gl3jt5hg5nkmzrzcguzg4e
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