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Ensembles for unsupervised outlier detection
<span title="2014-03-17">2014</span>
<i title="Association for Computing Machinery (ACM)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5ptshywu7rdklpcx3m6sfans3e" style="color: black;">SIGKDD Explorations</a>
</i>
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even clustering ensembles). ...
Complementary to his points, here we focus on the core ingredients for building an outlier ensemble, discuss the first steps taken in the literature, and identify challenges for future research. ...
detection ensembles as mentioned above for better clustering ensembles based on relative validity criteria. ...
<span class="external-identifiers">
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</span>
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Ensemble Learning Enhanced Stepwise Cluster Analysis for River Ice Breakup Date Forecasting
<span title="">2019</span>
<i title="International Society for Environmental Information Science (ISEIS)">
Journal of Environmental Informatics Letters
</i>
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A site on a representative river prone to river ice flooding in Alberta, Canada is selected to demonstrate the effectiveness of the proposed SCAE. ...
relatively important indicators of BD. ...
This study is financially supported by National Natural Science Foundation of China (61601522), and the Hundred Talents Program of Sun YatSen University. ...
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On Hyperparameter Search in Cluster Ensembles
[article]
<span title="2018-03-29">2018</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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quality measure for clustering problems. ...
The lack of robust and universally applicable cluster validity scores often makes the algorithm selection and hyperparameter evaluation a tough guess. ...
A possible explanation for this effect is the influence of the substantial number of unreasonable clusterings in the ensemble on C * (see Figure 3) . ...
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THE ENSEMBLE METHOD DEVELOPMENT OF CLASSIFICATION OF THE COMPUTER SYSTEM STATE BASED ON DECISIONS TREES
<span title="2020-10-05">2020</span>
<i title="National Technical University Kharkiv Polytechnic Institute">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qe4azhfienaffmupjqgxdv5qvm" style="color: black;">Advanced Information Systems</a>
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The purpose of the article is development of a method for classifying a computer system anomalous state based on ensemble methods. ...
Task: To investigate the usage of algorithms for building decision trees: REPTree, Random Tree, J48, HoeffdingTree, DecisionStump and bagging and boosting decision tree ensembles to identify a computer ...
The most effective, both for boosting and begging ensembles, is a classifier based on the J48 decision tree construction algorithm. ...
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Clustering ensemble selection for categorical data based on internal validity indices
<span title="">2017</span>
<i title="Elsevier BV">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jm6w2xclfzguxnhmnmq5omebpi" style="color: black;">Pattern Recognition</a>
</i>
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Clustering ensemble selection is an effective technique for improving the quality of clustering results. ...
To solve this problem, a new clustering ensemble selection algorithm for categorical data is presented. ...
Conclusion and future work To improve the performance of the ensemble clustering algorithms for categorical data, a novel selection strategy, namely Sum of Internal Validity Indices with Diversity (SIVID ...
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A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines
<span title="">2017</span>
<i title="Elsevier BV">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eqzh5s6rlnf5hginbk2cz4aatm" style="color: black;">Cell Systems</a>
</i>
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Finally, constructing an ensemble of models developed by many different groups is a robust strategy for achieving predictive accuracy on par with the best individual modeling approach. ...
clustering on mRNA expression data of signature genes. ...
Table 1 Top 10 GSEA Results of Gene Predictability Averaged over All Teams in each Sub-challenge ...
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<a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28988802">pmid:28988802</a>
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Predicting class-imbalanced business risk using resampling, regularization, and model ensembling algorithms
[article]
<span title="2019-03-13">2019</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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Area Under the Receiver Operating Characteristic Curve (AUC of ROC) is used for model comparison based on 10-fold cross validation. ...
Two ensembling techniques, including Bagging and Boosting, are applied on the DT classifier for further model improvement. ...
Decision tree and model ensembling Some studies point out that one of the reasons DT is not efficient for imbalanced data is, in the building of DT, the instance space is partitioned into smaller and smaller ...
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Ensembling Adaptively Constructed Polynomial Regression Models
<span title="2008-02-20">2008</span>
<i title="Zenodo">
Zenodo
</i>
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It lets the model building method itself construct the basis functions necessary for creating a model of arbitrary complexity with adequate predictive performance. ...
We try to correct these issues by model post-evaluation using Cross-Validation and model ensembling. ...
The validation set, however, is used for a postevaluation (using MSE in validation data) of the best models of each iteration of the search and for selection of the one final best model. ...
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Prognosis of muscular dystrophy with extrinsic and intrinsic descriptors through ensemble learning
<span title="">2017</span>
<i title="The Scientific and Technological Research Council of Turkey">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ewkcv4t6w5f2pc7j2w426gox7a" style="color: black;">Turkish Journal of Electrical Engineering and Computer Sciences</a>
</i>
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Experiments showed high accuracy for the models built using LibD3C classifier, which proves that ensemble learning is effective for predicting disease. ...
Furthermore, this paper analyzes the implementation of ensemble-learning classifiers based on features related to synonymous and nonsynonymous mutations, in order to detect muscular dystrophy performed ...
LibD3C is a type of ensemble classifier with a clustering and dynamic selection strategy. ...
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FReM – Scalable and stable decoding with fast regularized ensemble of models
<span title="">2018</span>
<i title="Elsevier BV">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sa477uo7lveh7hchpikpixop5u" style="color: black;">NeuroImage</a>
</i>
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Experiments on a large number of brain imaging datasets show that our combination of voxel clustering and model ensembling improves decoding maps stability and reduces the variance of prediction accuracy ...
We build upon very fast dimension reduction with spatial structure and model ensembling to achieve decoders that are fast on large datasets and increase the stability of the predictions and the maps. ...
Acknowledgment This project has received funding from the European Union's Horizon 2020 Framework Programme for Research and Innovation under Grant Agreement No 720270 (Human Brain Project SGA1). ...
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Software Defect Prediction Using Ensemble Learning: An ANP Based Evaluation Method
<span title="2018-09-30">2018</span>
<i title="Faculty of Engineering, Federal University Oye-Ekiti">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7xkejfxolneo7e3ud2nalkxvqi" style="color: black;">FUOYE Journal of Engineering and Technology</a>
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In essence, it is valid to say that before deciding which model or classifier is better for software defect prediction, all performance metrics should be considered.Keywords— Data mining, Machine Learning ...
This is because most studies on SDP consider the accuracy of the model or classifier above other performance metrics. ...
ACKNOWLEDGMENT The authors wish to thank the reviewers of this article for their comments and corrections. ...
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.46792/fuoyejet.v3i2.200">doi:10.46792/fuoyejet.v3i2.200</a>
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Spectral Clustering-based Classification
<span title="">2015</span>
<i title="IEEE">
2015 5th International Conference on Computer and Knowledge Engineering (ICCKE)
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In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with consideration for the confidentiality of data; i.e. it is the negotiations among ...
The utility of the proposed consensus strategy is examined on 18 datasets from the UCI repository in terms of clustering accuracy and speed-up against the centralized version of FCM. ...
Second is the relative values of local FCM cost function that is inversely proportional to the relative clustering validation of one machine with others. ...
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Mid-to-Long Range Wind Forecast in Brazil Using Numerical Modeling and Neural Networks
<span title="2022-04-22">2022</span>
<i title="MDPI AG">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/m4xowwt2dbhldc3pbk33me6wii" style="color: black;">Wind</a>
</i>
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The focus of this study was on the post-processing of NNs, in terms of data structure, dimensionality, architecture, training strategy, and validation. ...
By preserving the simplicity and small dimension of the NN model, it was possible to build an ensemble of NNs (20 members) that significantly improved the forecasts. ...
Conflicts of Interest: The authors declare no conflict of interest. Wind 2022, 2 ...
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Kernel-based detection of coincidentally correct test cases to improve fault localisation effectiveness
<span title="">2018</span>
<i title="Inderscience Publishers">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pa3v56u3lbefppd4isdzf6livm" style="color: black;">International Journal of Applied Pattern Recognition</a>
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<span title="2018-03-25">2018</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1803.09226v1">arXiv:1803.09226v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nuije3habfekfosylcvv6rwdsu">fatcat:nuije3habfekfosylcvv6rwdsu</a> </span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1803.09226v1">arXiv:1803.09226v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nuije3habfekfosylcvv6rwdsu">fatcat:nuije3habfekfosylcvv6rwdsu</a> </span>
This article aims at improving SBFL effectiveness by mitigating the effect of CC test cases. ...
The results show that our method can effectively improve the performance of SBFL techniques. ...
The third strategy partitions the ' into two clusters based on the similarity of the suspicious ' they execute, then ignores one of them based on further criteria. ...
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Clustering Validity Indices Evaluation with Regard to Semantic Homogeneity
<span title="2016-10-02">2016</span>
<i title="PTI">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zbznsfsymfchdlmdsmo2bglth4" style="color: black;">Position Papers of the 2016 Federated Conference on Computer Science and Information Systems</a>
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Clustering validity indices are methods for examining and assessing the quality of data clustering results. ...
The approach is applied to a problem of selecting a suitable clustering validity index for a real-world task of clustering biomedical articles using the MeSH ontology. ...
In [9] multiple strategies of building ensembles of clustering validity indices are evaluated. ...
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