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Multivariate Comparison of Classification Algorithms [article]

Olcay Taner Yildiz, Ethem Alpaydin
2014 arXiv   pre-print
In multivariate tests, comparison is done using multiple measures simultaneously.  ...  We use Hotelling's multivariate T^2 test for comparing two algorithms, and when we have three or more algorithms we use the multivariate analysis of variance (MANOVA) followed by pairwise post hoc tests  ...  Acknowledgments This work is supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under Grant EEEAG 109E186.  ... 
arXiv:1409.4566v1 fatcat:4fzaf4l6i5b2zgz7xaiv7od66m


Amanpreet Singh, DashmeetKaur Sethi, Karneet Singh, Lakshay Sharma, Poonam Narang
2017 International Journal of Advanced Research  
This project is a brief comparison of the classification algorithms such as random forest and Support vector machines applied on a multivariate time series, it focusses at comparing the error rate of the  ...  above stated algorithm for different sizes of dataset, so that one can efficiently choose an algorithm and classification when wanting to study a multivariate time series.  ...  This project is a brief comparison of the classification algorithms such as random forest and Support vector machines applied on a multivariate time series, it focusses at comparing the error rate of the  ... 
doi:10.21474/ijar01/4338 fatcat:hxmajmk62ne3bcv4yh22h4w7kq

Diagnosis of Out-of-control Signals in Multivariate Statistical Process Control Based on Bagging and Decision Tree

Jing Jiang, Hua-Ming Song
2017 Asian Business Research  
The experimental results show that our method could improve the accuracy of classification and is superior to other methods in terms of diagnosing out-of-control signals in multivariate statistical process  ...  Then we will integrate the classification results of multiple classifiers to determine the final classification.  ...  The classification results for various shifts values are summarized in Table 2 . Comparison of results with previous studies.  ... 
doi:10.20849/abr.v2i2.147 fatcat:5efu5o3p35hhzeffqq5rj37ddi

Simultaneous prediction of multiple outcomes using revised stacking algorithms [article]

Li Xing, Mary Lesperance, Xuekui Zhang
2019 arXiv   pre-print
Results: We propose two variations of a stacking algorithm which borrow information among multiple prediction tasks to improve multivariate prediction performance.  ...  The most attractive feature of our proposed methods is the flexibility with which complex multivariate prediction models can be constructed using any univariate prediction models.  ...  Acknowledgements Authors thank Professor Dominik Heider (University of Marburg) for his helpful discussion about pre-processing data from HIV data base.  ... 
arXiv:1901.10153v1 fatcat:42blh2runzcg3m4tkhix6obcx4

Functional Trees

João Gama
2004 Machine Learning  
In the context of classification problems, algorithms that generate multivariate trees are able to explore multiple representation languages by using decision tests based on a combination of attributes  ...  We observe that in the datasets under study and for classification and regression, the use of multivariate decision nodes has more impact in the bias component of the error, while the use of multivariate  ...  Acknowledgments Thanks to the detailed comments of the editor and the anonymous reviewers that much improved the text.  ... 
doi:10.1023/b:mach.0000027782.67192.13 fatcat:2docnsl24bdprcehlxzr4d3bzm

A Shapelet Transform for Multivariate Time Series Classification [article]

Aaron Bostrom, Anthony Bagnall
2017 arXiv   pre-print
We demonstrate that multivariate shapelets are not significantly worse than other state-of-the-art algorithms.  ...  We create a unified set of data to benchmark our work on, and compare with three other algorithms.  ...  We define three multivariate shapelet algorithms and evaluate a number of classifiers to build a large comparison of algorithms on 22 MTSC datasets.  ... 
arXiv:1712.06428v1 fatcat:hqdus3jnizgmrpgjn3chq23j4q

Rough Set Approach to Multivariate Decision Trees Inducing

Dianhong Wang, Xingwen Liu, Liangxiao Jiang, Xiaoting Zhang, Yongguang Zhao
2012 Journal of Computers  
The experimental results indicate that RSMDT algorithm significantly outperforms the comparison classification algorithms with improved classification accuracy, relatively small tree size, and shorter  ...  Aimed at the problem of huge computation, large tree size and over-fitting of the testing data for multivariate decision tree (MDT) algorithms, we proposed a novel roughset-based multivariate decision  ...  Summary on comparisons of tree size (in number of leaves) of six algorithms Table 6 . 6 Experimental results on classification accuracy (%) and standard deviationTable 7.  ... 
doi:10.4304/jcp.7.4.870-879 fatcat:ykomhgqxt5b4veoiaigw4djvue

Classification of Multivariate Data Sets without Missing Values Using Memory Based Classifiers - An Effectiveness Evaluation

C. Lakshmi Devasena
2013 International Journal of Artificial Intelligence & Applications  
A comparison of different memory based classifiers used and a practical guideline for selecting the renowned and most suited algorithm for a classification is presented.  ...  This work evaluates the performance of different memory based classifiers for classification of Multivariate data set without having Missing values from UCI machine learning repository using the open source  ...  ACKNOWLEDGEMENTS The author thanks the Management of Sphoorthy Engineering College and Faculties of CSE Department for the cooperation extended.  ... 
doi:10.5121/ijaia.2013.4110 fatcat:3suslf5wirbl3a3w43n4pbr3vq

Boosting for Real-Time Multivariate Time Series Classification

Haishuai Wang, Jun Wu
Experimental results on a real-world medical dataset demonstrate the effectiveness of the proposed methods.  ...  In this paper, we propose an ensemble boosting algorithm to classify abnormality surgery time series based on learning shapelet features.  ...  Fig. 1 shows the classification accuracy of all comparison algorithms in each chunk, in which the chunk size of time series stream is set to 100.  ... 
doi:10.1609/aaai.v31i1.11114 fatcat:wijy4qcznzgdnejr5bl6t7g74y

Frameworks for multivariate m-mediods based modeling and classification in Euclidean and general feature spaces

Shehzad Khalid, Shahid Razzaq
2012 Pattern Recognition  
The classification of new samples and anomaly detection is performed using a novel classification algorithm which can handle patterns with underlying multivariate probability distributions.  ...  This paper presents an extension of m-mediods based modeling technique to cater for multimodal distributions of sample within a pattern.  ...  The classification and anomaly detection algorithm, in the presence of multivariate settings, comprises the following steps: 1.  ... 
doi:10.1016/j.patcog.2011.08.021 fatcat:crl2xyqs5rc2znmaaoignknekm

Multivariate Statistical Tests for Comparing Classification Algorithms [chapter]

Olcay Taner Yıldız, Özlem Aslan, Ethem Alpaydın
2011 Lecture Notes in Computer Science  
The misclassification error which is usually used in tests to compare classification algorithms, does not make a distinction between the sources of error, namely, false positives and false negatives.  ...  We propose to use the pairwise test based on Hotelling's multivariate T 2 test to compare two algorithms or multivariate analysis of variance (MANOVA) to compare L > 2 algorithms.  ...  Pairwise Comparison Let us say we have two classification algorithms.  ... 
doi:10.1007/978-3-642-25566-3_1 fatcat:wnugkwzhlbc4xd6ykytcx2qpca

Page 3511 of Mathematical Reviews Vol. , Issue 82h [page]

1982 Mathematical Reviews  
Gordon, On the assessment and comparison of classifications (pp. 149-160); A. D.  ...  Author’s introduction: “In every type or technique of multivariate analysis there are usually two aspects: model and algorithm. Furthermore, there are two kinds of models: random and fixed.  ... 


S. Niazmardi, A. Safari, S. Homayouni
2017 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping.  ...  The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms.  ...  Stacking the images of a multivariate SITS to create a single image is another common practice for multivariate SITS data classification.  ... 
doi:10.5194/isprs-archives-xlii-4-w4-201-2017 fatcat:6qg5ml67nrapllelvzvshbvmfy


Carla E. Brodley, Paul E. Utgoff
2012 Machine Learning  
test, selecting the features to include in a test, and pruning of multivariate decision trees.  ...  This article addresses several issues for constructing multivariate decision trees: representing a multivariate test, including symbolic and numeric features, learning the coefficients of a multivariate  ...  The multivariate methods were each run ten times, because they train the linear machines using instances sampled randomly from the training data. 6.  ... 
doi:10.1023/a:1022607123649 fatcat:zvvogc2mnvgypk54dkjuvstfwm

Visual-Interactive Segmentation of Multivariate Time Series [article]

Jürgen Bernard, Eduard Dobermann, Markus Bögl, Martin Röhlig, Anna Vögele, Jörn Kohlhammer
2016 EuroVis Workshop on Visual Analytics (EuroVA)  
A similarity-preserving colormap further facilitates visual comparison and labeling of segments.  ...  We propose a Visual Analytics (VA) approach to address these challenges in the scope of human motion capture data, a special type of multivariate time series data.  ...  In the following, we give a brief overview of the related work in time series segmentation algorithms, as well as the visual analysis and comparison of segmented time series and of temporal patterns.  ... 
doi:10.2312/eurova.20161121 dblp:conf/vissym/BernardDBRVK16 fatcat:2njqhnw5kba4pin4p7hmwnvhou
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