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Decision Trees for Computer Go Features [chapter]

Francois van Niekerk, Steve Kroon
<span title="">2014</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jyopc6cf5ze5vipjlm4aztcffi" style="color: black;">Communications in Computer and Information Science</a> </i> &nbsp;
This paper investigates the feasibility of using decision trees to generate features for Computer Go.  ...  In Computer Go, these features are typically comprised of a number of hand-crafted heuristics and a collection of patterns, with weights for these features usually trained using data from high-level Go  ...  Acknowledgments The first author would like to thank the MIH Media Lab at Stellenbosch University for the use of their facilities and support of the work presented here.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-05428-5_4">doi:10.1007/978-3-319-05428-5_4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h4uztzvhtbenhdtuexvqwuoysq">fatcat:h4uztzvhtbenhdtuexvqwuoysq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170830021733/http://leafcloud.com/wp-content/uploads/2013/08/paper.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/78/e4/78e4d0fb4399519e88e393d0f961fdaf6ba37157.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-05428-5_4"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

A High-Performance Computing Implementation of Iterative Random Forest for the Creation of Predictive Expression Networks

Ashley Cliff, Jonathon Romero, David Kainer, Angelica Walker, Anna Furches, Daniel Jacobson
<span title="2019-12-02">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/githwx23ynaxdoftesby3jya3y" style="color: black;">Genes</a> </i> &nbsp;
In this paper, we present a high-performance computing (HPC)-capable implementation of Iterative Random Forest (iRF).  ...  Using this implementation, we also present a new method, iRF Leave One Out Prediction (iRF-LOOP), for the creation of Predictive Expression Networks on the order of 40,000 genes or more.  ...  reproduce the published form of this manuscript, or allow others to do so, for US Government purposes.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/genes10120996">doi:10.3390/genes10120996</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31810264">pmid:31810264</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hjbl2gfd5zfaxhtyj4hva6sg3a">fatcat:hjbl2gfd5zfaxhtyj4hva6sg3a</a> </span>
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Pruning Algorithm For The Minimum Rule Reduct Generation

Şahin Emrah Amrahov, Fatih Aybar, Serhat Doğan
<span title="2015-01-02">2015</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Create a tree of the features for the current object Step 3.  ...  step of finding one-feature rule reducts for 4 x and planning next related tree branchesFig. 6 Searching two features rule-reducts F1F2 for 4The next step is done for 4 F feature.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.1099228">doi:10.5281/zenodo.1099228</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yefhjo4gargj7c3sulh624vjzi">fatcat:yefhjo4gargj7c3sulh624vjzi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201223233034/https://zenodo.org/record/1099228/files/10000502.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/29/8c/298cc76f49df2330ce544e60c0d6a255147b4cc8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.1099228"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Image Annotations using Machine Learning and Features of ID3 Algorithm

D.V.N Harish, Y. Srinivas, K.N.V.S.S.K Rajesh, P. Anuradha
<span title="2011-07-31">2011</span> <i title="Foundation of Computer Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b637noqf3vhmhjevdfk3h5pdsu" style="color: black;">International Journal of Computer Applications</a> </i> &nbsp;
K –1993 { If the image having stripes [4] “Induction of Decision trees” J.R.QUINLAN Centre for Then it is a wild cat Advanced Computing Sciences, New South  ...  A subset of the training set called the window is chosen at random and a decision tree The feature set can be of two types either a local feature set or formed from it; this tree correctly classifies  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/3024-4090">doi:10.5120/3024-4090</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/anrkrhxdabdoda75c2iwr3z77y">fatcat:anrkrhxdabdoda75c2iwr3z77y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170813180541/http://www.ijcaonline.org/volume25/number5/pxc3874090.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/df/1d/df1ddf5d9ee5104f13c97187f19618dca36eaef4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/3024-4090"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Indecisive Trees for Classification and Prediction of Knee Osteoarthritis [chapter]

Luca Minciullo, Paul A. Bromiley, David T. Felson, Timothy F. Cootes
<span title="">2017</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
We demonstrate that replacing this with something less decisive, where some samples may go to both child nodes, can improve performance for both individual trees and whole forests.  ...  Each tree in the forest contains binary decision nodes that choose whether a sample should be passed to one of two child nodes.  ...  Acknowledgments The research leading to this results has received funding from EPSRC Centre for Doctoral Training grant 1512584.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-67389-9_33">doi:10.1007/978-3-319-67389-9_33</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pqsqt6pcanhmrdyeizubvynvgy">fatcat:pqsqt6pcanhmrdyeizubvynvgy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190223051016/http://pdfs.semanticscholar.org/424a/9a1c4dc09a89b7dd8b62e747d0c38990cb35.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/42/4a/424a9a1c4dc09a89b7dd8b62e747d0c38990cb35.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-67389-9_33"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Predictive Model on Employability of Applicants and Job Hopping using Machine Learning

Neeraj Khadilkar, Deepali Joshi
<span title="2017-08-17">2017</span> <i title="Foundation of Computer Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b637noqf3vhmhjevdfk3h5pdsu" style="color: black;">International Journal of Computer Applications</a> </i> &nbsp;
For employability prediction we got the highest accuracy for naïve based with 89% and for predicting whether the employee's going to quit the job or not we got the highest accuracy for decision tree with  ...  This would help in the institutions to assess whether they are producing employable students or not, also this would provide a support for organizations in screening bundles of applications and finding  ...  Minimal Optimization (SMO), Ensemble Methods and Decision Trees, to predict thee employability of Master of Computer Applications (MCA) students and find the algorithm which is best suited for this problem  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/ijca2017914966">doi:10.5120/ijca2017914966</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/f74euagjznhgbigcxeieicedve">fatcat:f74euagjznhgbigcxeieicedve</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180602105723/https://www.ijcaonline.org/archives/volume171/number1/khadilkar-2017-ijca-914966.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/da/bb/dabb74c16ca1415322f2fbb62cc876073ea06fc0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/ijca2017914966"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Estimating feature discriminant power in decision tree classifiers [chapter]

I. Gracia, F. Pla, F. J. Ferri, P. García
<span title="">1995</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;
In this work, a method is proposed to rank a given set of features in the particular case of Decision Tree classifiers, using the same information generated while constructing the tree.  ...  Feature Selection is an important phase in pattern recognition system design.  ...  And second, good feature selection criteria need so much computation that even polinomic-time algorithms are inapplicable for some problems. 613 Decision Trees (DT) are a particular and interesting type  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/3-540-60268-2_353">doi:10.1007/3-540-60268-2_353</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5up5tpt4b5gs3dbtnv7gxkqbfu">fatcat:5up5tpt4b5gs3dbtnv7gxkqbfu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20110719145800/http://marmota.dlsi.uji.es/WebBIB//papers/1995/Gracia-1995-CAIP.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/bf/44/bf4483ee82df0604d3854bb40208206503afec7d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/3-540-60268-2_353"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques

Adil Deniz Duru
<span title="2019-03-31">2019</span> <i title="Marmara University Journal of Science"> International Journal of Advances in Engineering and Pure Sciences </i> &nbsp;
The accuracy value obtained from KNN was found to be 0.94 while it was 0.88 for decision tree and SVM.  ...  EEG data were epoched with a duration of one second and power spectrum was computed for each time window.  ...  In our further studies, decision trees are going to be used as a preprocessing tool for the selection of most relevant features.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7240/jeps.459420">doi:10.7240/jeps.459420</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/b5mjn4qcrjekzgeirtj5s3dcnq">fatcat:b5mjn4qcrjekzgeirtj5s3dcnq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200314132011/https://dergipark.org.tr/en/download/article-file/693942" 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/b4/ba/b4ba4da6706236fb23b9fa33763b0ef7a24affc5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7240/jeps.459420"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Continuous track paths reveal additive evidence integration in multistep decision making

Cristian Buc Calderon, Myrtille Dewulf, Wim Gevers, Tom Verguts
<span title="2017-09-18">2017</span> <i title="Proceedings of the National Academy of Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nvtuoas5pbdsllkntnhizy4f4q" style="color: black;">Proceedings of the National Academy of Sciences of the United States of America</a> </i> &nbsp;
This illustrates a typical twostep tree path decision-making scenario (i.e., four potential tree paths; see Fig. 1A ).  ...  In each model, each tree path is associated with an evidence (E) accumulator (e.g., in Fig. 1A , there are four tree paths; we will use Fig. 1A and Supporting Information, Appendix A: Computational Models  ...  We thank Clay Holroyd, William Alexander, and the anonymous reviewers for valuable comments. C.B.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1073/pnas.1710913114">doi:10.1073/pnas.1710913114</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28923918">pmid:28923918</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5635910/">pmcid:PMC5635910</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lqju2lqsj5gvzngnul4ox3dcdi">fatcat:lqju2lqsj5gvzngnul4ox3dcdi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180723220648/https://biblio.ugent.be/publication/8533169/file/8533172.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/aa/cd/aacdfa2b504a0b27e746b0bd521a78b17f2e6a10.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1073/pnas.1710913114"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635910" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Decision Tree Method Using for Fetal State Classification from Cardiotography Data

Md Zannatul Arif, Rahate Ahmed, Umma Habiba Sadia, Mst Shanta Islam Tultul, Rocky Chakma
<span title="2020-03-31">2020</span> <i title="Vietnam National University Journal of Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xcspdqimfbgfrfhx6ap73kor4i" style="color: black;">Journal of Advanced Engineering and Computation</a> </i> &nbsp;
The motive of the investigation is analyzing the categorization of fetal state code from the Cardiographic data set based on decision tree method.  ...  Overall, the experimental results proved the viability of Classification and Regression Trees and its potential for further predictions.This is an Open Access article distributed under the terms of the  ...  Final variables FM is <0.1 which will go terminal node and this is the decision tree. Our total validate data set has 408 observations.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.25073/jaec.202041.273">doi:10.25073/jaec.202041.273</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/figfi2lqvngfdmb5fdmayopoku">fatcat:figfi2lqvngfdmb5fdmayopoku</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200402131250/http://jaec.vn/index.php/JAEC/article/download/273/124" 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/3f/f8/3ff8a14a553f3be1fac757642771e2e3000d7f07.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.25073/jaec.202041.273"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

A Comparative Study on Decision Tree and Random Forest Using R Tool
IJARCCE - Computer and Communication Engineering

Prajwala T R
<span title="2015-01-30">2015</span> <i title="Tejass Publisheers"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ezm4lr6uezhi5pfgr6xkdexmgy" style="color: black;">IJARCCE</a> </i> &nbsp;
This paper discusses two classification algorithms namely decision trees and Random forest.. Decision trees are powerful and popular tools for classification and prediction.  ...  Decision trees represent rules, which can be understood by humans and used in knowledge system such as database.  ...  The importance score for the i-th feature is computed by averaging the difference in error before and after the permutation for all the trees.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17148/ijarcce.2015.4142">doi:10.17148/ijarcce.2015.4142</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p4sdxrtksrh6beh6bdqk5vlvhy">fatcat:p4sdxrtksrh6beh6bdqk5vlvhy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180602211131/https://ijarcce.com/upload/2015/january/IJARCCE3L.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/d1/29/d1290d66ee661107c275b12d359a4f0e79de8d70.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17148/ijarcce.2015.4142"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

End-to-end Learning of Deterministic Decision Trees [article]

Thomas Hehn, Fred A. Hamprecht
<span title="2017-12-07">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In summary, we present the first end-to-end learning scheme for deterministic decision trees and present results on par with or superior to published standard oblique decision tree algorithms.  ...  Conventional decision trees have a number of favorable properties, including interpretability, a small computational footprint and the ability to learn from little training data.  ...  Related work Decision trees and decision tree ensembles, such as random forests [1] , are widely used for computer vision [4] and have proven effective on a variety of classification tasks [7] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1712.02743v1">arXiv:1712.02743v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vbebddzmdnbhnb7jxvyguoie3i">fatcat:vbebddzmdnbhnb7jxvyguoie3i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200823082249/https://arxiv.org/pdf/1712.02743v1.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/8c/8c/8c8c9280f6d2d06b05c1c18f4f7ee08621357163.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1712.02743v1" 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>

The decision tree classifier: Design and potential

Philip H. Swain, Hans Hauska
<span title="">1977</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wxwwgiv25rfzhgrk4r3nrbovia" style="color: black;">Geoscience Electronics, IEEE Transactions on</a> </i> &nbsp;
The two main methods to design decision trees are presented and discussed along with some experimental results. An attempt is made to describe an Applicable Logic for the design of decision trees.  ...  The basic concepts of a multi-stage classification strategy, the decision tree classifier, are presented.  ...  The computation of the evaluation function is now reduced to estimates of the computation time, the probability of the classification path to go through this particular node and the probability of error  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tge.1977.6498972">doi:10.1109/tge.1977.6498972</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/okbjztudhnabvec52lguib25eu">fatcat:okbjztudhnabvec52lguib25eu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180719084135/https://docs.lib.purdue.edu/cgi/viewcontent.cgi?referer=&amp;httpsredir=1&amp;article=1046&amp;context=lars_symp" 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/10/6c/106cff05752b373bf098437770be16d870df5cd5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tge.1977.6498972"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

An Interactive Machine Learning Framework [article]

Teng Lee, James Johnson, Steve Cheng
<span title="2016-10-18">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
It combines the advantages of both ML in big data statistics and human in decision making based on domain knowledge.  ...  We develop a user friendly interface for this novel learning method, and apply it to two datasets collected from real applications.  ...  TBT is going to build GBT model for prediction tasks for the two datasets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1610.05463v1">arXiv:1610.05463v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zyebyuhu6bdsppsp4mzq6v5flm">fatcat:zyebyuhu6bdsppsp4mzq6v5flm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200826073249/https://arxiv.org/pdf/1610.05463v1.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/2d/b3/2db3dfd7e6959a707de7c9367340f2a1774abbc2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1610.05463v1" 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>

A fuzzy based enhancement on prism and J48 classifier prediction of student performance

Sasi Regha. R, Uma Rani. R
<span title="2018-05-21">2018</span> <i title="Association of Computer, Communication and Education for National Triumph Social and Welfare Society (ACCENTS)"> International Journal of Advanced Technology and Engineering Exploration </i> &nbsp;
The attribute with maximum weight value and fuzzified value of features are used for constructing tree of prism and J48 classifiers.  ...  A modified computed aided design of experiments (MCADEX) using Kullback-Leibler divergence and modified principal component analysis (MPCA) was proposed for selecting set of samples to improve the prediction  ...  Go to step 1 until the entire classes are examined (b) J48 J48 classifier is a simple C4.5 decision tree for classification. This classifier creates a binary tree.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.19101/ijatee.2018.542014">doi:10.19101/ijatee.2018.542014</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jwobaeiumrg4lhb2wvc5mtn25q">fatcat:jwobaeiumrg4lhb2wvc5mtn25q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200322115452/https://www.accentsjournals.org/PaperDirectory/Journal/IJATEE/2018/5/3.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/ec/29/ec29ba742177b599e639d038592ea834c20de864.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.19101/ijatee.2018.542014"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>
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