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Is a Data-Driven Approach still Better than Random Choice with Naive Bayes classifiers? [article]

Piotr Szymański, Tomasz Kajdanowicz
<span title="2017-02-13">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We study the performance of data-driven, a priori and random approaches to label space partitioning for multi-label classification with a Gaussian Naive Bayes classifier.  ...  The advantage of data-driven methods against a priori methods with a weak classifier is lesser than when tree classifiers are used.  ...  We thus repeat the research questions we have asked in the case of tree-based classifiers, this time for Naive Bayes based classifiers: RH : Data-driven approach is significantly better than random (α  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1702.04013v1">arXiv:1702.04013v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uabogeg74jbijhzl3g2lw3r3ey">fatcat:uabogeg74jbijhzl3g2lw3r3ey</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191019221813/https://arxiv.org/pdf/1702.04013v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/12/a9/12a9fafd5d02329b1fd4c91fec4f8d17089aeaa1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1702.04013v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Learning Ontology-Aware Classifiers [chapter]

Jun Zhang, Doina Caragea, Vasant Honavar
<span title="">2005</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
A better approach is to perform necessary analysis on data where data and computational resources are available and only necessary information is transmitted to build a classifier.  ...  We also replace B with h, the corresponding Naïve Bayes classifier with regard to a chosen global cut.  ...  perform regularization at high levels of abstraction, AVT-NBL shows much better performance than AVT-NBL when applied to semantically heterogeneous data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11563983_26">doi:10.1007/11563983_26</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5k3gutiltfcobltvcly2p4qnu4">fatcat:5k3gutiltfcobltvcly2p4qnu4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190503040518/https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=2787&amp;context=rtd" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/68/57/685780d9cdf3d29d9a597a38f36e0ee193912cdd.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11563983_26"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Differentiable TAN Structure Learning for Bayesian Network Classifiers [article]

Wolfgang Roth, Franz Pernkopf
<span title="2020-08-21">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we consider learning of tree-augmented naive Bayes (TAN) structures for Bayesian network classifiers with discrete input features.  ...  Learning the structure of Bayesian networks is a difficult combinatorial optimization problem.  ...  Note that the data-driven Chow-Liu structure does not outperform TAN Random on all datasets, and there is even a large performance gap on usps where we observed overfitting.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.09566v1">arXiv:2008.09566v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jjoeikh6zreiphrp7yiieg6jzu">fatcat:jjoeikh6zreiphrp7yiieg6jzu</a> </span>
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Quantile-based classifiers

C. Hennig, C. Viroli
<span title="2016-05-24">2016</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6oeltljhrzfq7brjdtp2wqehpu" style="color: black;">Biometrika</a> </i> &nbsp;
Classification with small samples of high-dimensional data is important in many areas.  ...  It is shown that this choice is consistent for the classification rule with the asymptotically optimal quantile, and that, under some assumptions, when the number of variables goes to infinity, the 15  ...  The fourth scenario with Beta distributions differing between variables and classes within variables is again generally dominated by the quantile classifiers, with only the naive Bayes classifier achieving  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/biomet/asw015">doi:10.1093/biomet/asw015</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/27279668">pmid:27279668</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wdt6gbdqjve2bf4rr7gyd5qn2m">fatcat:wdt6gbdqjve2bf4rr7gyd5qn2m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180722063222/http://discovery.ucl.ac.uk/1492790/8/Hennig%20Viroli%202016%20Quantile-based%20classifiers.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3c/02/3c0231f6d5cabea6972792b2de3a9f705ec1084e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/biomet/asw015"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> oup.com </button> </a>

Biological classification with RNA-Seq data: Can alternative splicing enhance machine learning classifier? [article]

Nathan T. Johnson, Andi Dhroso, Katelyn J. Hughes, Dmitry Korkin
<span title="2017-06-18">2017</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
RNA sequencing (RNA-Seq) is becoming a prevalent approach to quantify gene expression, and is expected to gain better insights to a number of biological and biomedical questions, compared to the DNA microarrays  ...  We hypothesize that the isoform-level expression data is more informative for biological classification tasks than the gene-level expression data.  ...  Lane Harrison for useful suggestions about the biological data visualization.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/146340">doi:10.1101/146340</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2oqxplnwkbfk7hvbfv4gtrlnmy">fatcat:2oqxplnwkbfk7hvbfv4gtrlnmy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180720050256/https://www.biorxiv.org/content/biorxiv/early/2017/06/18/146340.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/45/5f/455f2cdbea4adc1a2599a40e150bb94c3b9bb4f2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/146340"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Probability-driven scoring functions in combining linear classifiers [article]

Pawel Trajdos, Robert Burduk
<span title="2021-09-16">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Namely, we proposed a probability-driven scoring function which shape depends on the orientation of the decision hyperplanes generated by the base classifiers.  ...  This research is aimed at building a new fusion method dedicated to the ensemble of linear classifiers. The fusion scheme uses both measurement space and geometrical space.  ...  The nonparametric estimator seems to be a better choice than a parametric one related to the arbitrary chosen distribution (The Gaussian one in this study).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.07815v1">arXiv:2109.07815v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sffnhe7z35c5jdjkamfn2aiyyi">fatcat:sffnhe7z35c5jdjkamfn2aiyyi</a> </span>
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Morphological classifiers

É.O. Rodrigues, A. Conci, P. Liatsis
<span title="">2018</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jm6w2xclfzguxnhmnmq5omebpi" style="color: black;">Pattern Recognition</a> </i> &nbsp;
MCs tied or outperformed 14 well established classifiers in 5 out of 8 datasets. In all occasions, the obtained accuracies were higher than the average accuracy obtained with all classifiers.  ...  The outcomes of this aggregation are classifiers that may preserve shape characteristics of classes, subject to the choice of a stopping criterion and structuring element.  ...  Here, we refer to them as Bayesbased algorithms, with the popular algorithms being the Naive Bayes [27, 28] and Bayes Net [6, 29] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.patcog.2018.06.010">doi:10.1016/j.patcog.2018.06.010</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/edhf5f5cpzgvhmhn64elb2kpgi">fatcat:edhf5f5cpzgvhmhn64elb2kpgi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211231075504/https://arxiv.org/ftp/arxiv/papers/2112/2112.12262.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/33/8e/338e80d24c1f5c4ef02158113f8d5f16d4c83de1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.patcog.2018.06.010"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Quantile-based classifiers [article]

Christian Hennig, Cinzia Viroli
<span title="2013-11-12">2013</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The optimal quantile classifier performs very well in a comprehensive simulation study and a real data set from chemistry (classification of bioaerosols) compared to nine other classifiers, including the  ...  Quantile classifiers for potentially high-dimensional data are defined by classifying an observation according to a sum of appropriately weighted component-wise distances of the components of the observation  ...  The approach recommended here has the advantage that the choice of θ is still governed by a one-dimensional optimization of the overall misclassification rate, and that there is no issue scaling variable-wise  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1303.1282v2">arXiv:1303.1282v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ipw7wjjhc5hjrg55abqlujeqny">fatcat:ipw7wjjhc5hjrg55abqlujeqny</a> </span>
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Learning Probabilistic Transfer Functions: A Comparative Study of Classifiers

K. P. Soundararajan, T. Schultz
<span title="">2015</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/p2lpq6bugfcqxk44anrm6yki4m" style="color: black;">Computer graphics forum (Print)</a> </i> &nbsp;
To this end, we extend a previous intelligent system approach to volume rendering, and we systematically compare five supervised classification techniques -Gaussian Naive Bayes, k Nearest Neighbor, Support  ...  Vector Machines, Neural Networks, and Random Forests -with respect to probabilistic classification, support for multiple materials, interactive performance, robustness to unreliable input, and easy parameter  ...  Acknowledgements We would like to thank Max Hermann (University of Bonn) for providing a volume rendering framework; Jürgen Gall (University of Bonn) for a discussion on random forests; Tobias Schmidt-Wilcke  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1111/cgf.12623">doi:10.1111/cgf.12623</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cnn7vhk3pfcobb3wusoi2sxiwy">fatcat:cnn7vhk3pfcobb3wusoi2sxiwy</a> </span>
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Bayes Net Classifiers For Prediction Of Renal Graft Status And Survival Period

Jiakai Li, Gursel Serpen, Steven Selman, Matt Franchetti, Mike Riesen, Cynthia Schneider
<span title="2010-03-25">2010</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Two separate classifiers were induced from the data set, one to predict the status of the graft as either failed or living, and a second classifier to predict the graft survival period.  ...  The Bayes net classifiers were developed using the Weka machine learning software workbench.  ...  A number of Bayes net classifiers performed better than the 60% prediction rate. The highest performing BBN models used versions of local K2 and hill climber for structure learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.1081243">doi:10.5281/zenodo.1081243</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2y7qahofcjhqzfwyfudhhzv5qe">fatcat:2y7qahofcjhqzfwyfudhhzv5qe</a> </span>
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"Read My Lips": Using Automatic Text Analysis to Classify Politicians by Party and Ideology [article]

Eitan Sapiro-Gheiler
<span title="2018-09-03">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Applying machine learning techniques, we use this data to automatically classify senators according to party, obtaining accuracy in the 70-95% range depending on the specific method used.  ...  Text-based predictions are less accurate than those based on voting behavior, supporting the theory that roll-call votes represent greater commitment on the part of politicians and are thus a more accurate  ...  Node choice is determined by an algorithm based around impurity, the degree to which the branches of a node line up with the classes for classification. The second method is a naïve Bayes classifier.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1809.00741v1">arXiv:1809.00741v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2zaajz2oorc5pb7ftu5ecn7zxi">fatcat:2zaajz2oorc5pb7ftu5ecn7zxi</a> </span>
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Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?

Nathan T. Johnson, Andi Dhroso, Katelyn J. Hughes, Dmitry Korkin
<span title="2018-06-25">2018</span> <i title="Cold Spring Harbor Laboratory"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5r3smv4gwnggfemxt2y4fgjxqa" style="color: black;">RNA: A publication of the RNA Society</a> </i> &nbsp;
RNA sequencing (RNA-seq) is becoming a prevalent approach to quantify gene expression and is expected to gain better insights into a number of biological and biomedical questions compared to DNA microarrays  ...  We find that for every single classification problem, the transcript-based classifiers outperform or are comparable with gene expression-based methods.  ...  Lane Harrison for useful suggestions about the biological data visualization. This work has been supported by the National Science Foundation (DBI-0845196 to D.K.).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1261/rna.062802.117">doi:10.1261/rna.062802.117</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29941426">pmid:29941426</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6097660/">pmcid:PMC6097660</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qvf4pm6mqjcenculk7ir7foms4">fatcat:qvf4pm6mqjcenculk7ir7foms4</a> </span>
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Naïve Bayes Classifier-Assisted Least Loaded Routing for Circuit-Switched Networks

Longfei Li, Ya Zhang, Wei Chen, Sanjay K. Bose, Moshe Zukerman, Gangxiang Shen
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
This paper develops a new and very different methodology, by incorporating a supervised naïve Bayes (NB) classifier, to assist least loaded (LL) routing and to further improve its performance that has  ...  INDEX TERMS Machine learning, naïve Bayes classifier, least loaded routing, blocking probability, circuit switched network.  ...  Moreover, the final blocking performance of the naïve Bayes classifier-assisted LL routing algorithm is always better than that of the other two routing algorithms.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2892063">doi:10.1109/access.2019.2892063</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zpjso6hbbba2ndiozzpsodfggi">fatcat:zpjso6hbbba2ndiozzpsodfggi</a> </span>
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Learning probabilistic classifiers for human–computer interaction applications

Nicu Sebe, Ira Cohen, Fabio G. Cozman, Theo Gevers, Thomas S. Huang
<span title="2005-05-10">2005</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/c2kkfk736jeeje2nj5asysaflq" style="color: black;">Multimedia Systems</a> </i> &nbsp;
In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data for HCI applications.  ...  It is argued that to truly achieve effective human-computer intelligent interaction, the computer should be able to interact naturally with the user, similar to the way HCI takes place.  ...  Start with Naive Bayes and TAN classifiers, learn with only labeled data, and test whether the model is correct by learning with the unlabeled data, using EM and EM-TAN.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00530-005-0177-4">doi:10.1007/s00530-005-0177-4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/elku2ivr2bczvkbeuvxkyhvsei">fatcat:elku2ivr2bczvkbeuvxkyhvsei</a> </span>
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Prediction of Tunnel Face Stability Using a Naive Bayes Classifier

Li, Li
<span title="2019-10-02">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
After that, the probability density functions (PDF) of the features are identified, and a naive Bayes classifier is constructed with the prior probabilities of the stable and the unstable state.  ...  This paper develops a convenient approach for facilitating the prediction of tunnel face stability in the framework of Bayesian theorem.  ...  In the same way, the error rate of the constructed naive Bayes classifier can be estimated with the entire training data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app9194139">doi:10.3390/app9194139</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zb2q5byx6zd7ndqsilylq7pibe">fatcat:zb2q5byx6zd7ndqsilylq7pibe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200210082709/https://res.mdpi.com/d_attachment/applsci/applsci-09-04139/article_deploy/applsci-09-04139-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e8/ca/e8cacc8ecb99f861ccb64e6cbe65c774b86bab63.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app9194139"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>
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