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Tree ensembles for predicting structured outputs

Dragi Kocev, Celine Vens, Jan Struyf, Sašo Džeroski
2013 Pattern Recognition  
The types of outputs considered correspond to different predictive modeling tasks: multi-target regression, multi-target classification, and hierarchical multi-label classification.  ...  We propose to build ensemble models consisting of predictive clustering trees, which generalize classification trees: these have been used for predicting different types of structured outputs, both locally  ...  We used the predictive clustering framework both for predicting multiple targets and for hierarchical multi-label classification.  ... 
doi:10.1016/j.patcog.2012.09.023 fatcat:u5x22ugkgffy7puzqdmru374sy

Oblique Predictive Clustering Trees [article]

Tomaž Stepišnik, Dragi Kocev
2020 arXiv   pre-print
Predictive clustering trees (PCTs) are a well established generalization of standard decision trees, which can be used to solve a variety of predictive modeling tasks, including structured output prediction  ...  In this paper, we propose oblique predictive clustering trees, capable of addressing these limitations.  ...  , hierarchical multi-label classification, single-target regression, and multi-target regression.  ... 
arXiv:2007.13617v2 fatcat:kvb2hrcclbgyrm6e3nghfcpisy

Multi-scale Mining of fMRI Data with Hierarchical Structured Sparsity

Rodolphe Jenatton, Alexandre Gramfort, Vincent Michel, Guillaume Obozinski, Francis Bach, Bertrand Thirion
2011 2011 International Workshop on Pattern Recognition in NeuroImaging  
Specifically, the penalization we use is constructed from a tree that is obtained by spatially-constrained agglomerative clustering.  ...  In this paper, we consider a sparse hierarchical structured regularization.  ...  ., in bioinformatics, to exploit the tree structure of gene networks for multi-task regression [24] , and also for topic models and image inpainting [22] .  ... 
doi:10.1109/prni.2011.15 dblp:conf/prni/JenattonGMOBT11 fatcat:hmwhz3v6qnfmvjbepuvzotsafi

Multiscale Mining of fMRI Data with Hierarchical Structured Sparsity

Rodolphe Jenatton, Alexandre Gramfort, Vincent Michel, Guillaume Obozinski, Evelyn Eger, Francis Bach, Bertrand Thirion
2012 SIAM Journal of Imaging Sciences  
Specifically, the penalization we use is constructed from a tree that is obtained by spatially-constrained agglomerative clustering.  ...  In this paper, we consider a sparse hierarchical structured regularization.  ...  ., in bioinformatics, to exploit the tree structure of gene networks for multi-task regression [24] , and also for topic models and image inpainting [22] .  ... 
doi:10.1137/110832380 fatcat:5wytiyengrd6fl447d27eo5a4m

Learning Predictive Clustering Rules [chapter]

Bernard Ženko, Sašo Džeroski, Jan Struyf
2006 Lecture Notes in Computer Science  
Here we address the task of predictive clustering, which contains elements of both and generalizes them to some extent. Predictive clustering has been mainly evaluated in the context of trees.  ...  We propose a system for learning these predictive clustering rules, which is based on a heuristic sequential covering algorithm.  ...  Jan Struyf is a post-doctoral fellow of the Fund for Scientific Research of Flanders (FWO-Vlaanderen). Many thanks to Hendrick Blockeel for his useful comments on an earlier draft of this paper.  ... 
doi:10.1007/11733492_14 fatcat:axil6jm5zbhn3el5cke3obxjny

Semi-supervised oblique predictive clustering trees

Tomaž Stepišnik, Dragi Kocev
2021 PeerJ Computer Science  
Semi-supervised predictive clustering trees (SSL-PCTs) are a prominent method for semi-supervised learning that achieves good performance on various predictive modeling tasks, including structured output  ...  In contrast to axis-parallel trees, which only use individual features to split the data, oblique predictive clustering trees (SPYCTs) use linear combinations of features.  ...  ), and hierarchical multi-label classification (HMLC), single-target regression (STR) and multi-target regression (MTR).  ... 
doi:10.7717/peerj-cs.506 pmid:33987461 pmcid:PMC8101547 fatcat:saqwqyeg4zguvlnb7jqbufsioi

Learning habitat models for the diatom community in Lake Prespa

Dragi Kocev, Andreja Naumoski, Kosta Mitreski, Svetislav Krstić, Sašo Džeroski
2010 Ecological Modelling  
We learn two multi-target regression trees: one for modelling the complete community and the other for the top 10 most abundant diatoms.  ...  We use two machine learning techniques to model the data: regression trees and multi-target regression trees.  ...  Acknowledgement This work was supported by the bilateral project between Slovenia and Macedonia (Grant number 17/2007-2008), titled "Knowledge Discovery for Ecological Modelling of Lake Ecosystems".  ... 
doi:10.1016/j.ecolmodel.2009.09.002 fatcat:pxmnwnvamvbclmwusajnron3yq

Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity [article]

Rodolphe Jenatton, Alexandre Gramfort, Vincent Michel (LNAO, INRIA Saclay - Ile de France), Guillaume Obozinski (LIENS, INRIA Paris - Rocquencourt), Evelyn Eger, Francis Bach , Bertrand Thirion
2011 arXiv   pre-print
Specifically, the penalization we use is constructed from a tree that is obtained by spatially-constrained agglomerative clustering.  ...  In this paper, we consider a sparse hierarchical structured regularization.  ...  Tree 2 (ρ = 1)-ML Median fraction of non-zeros (%) Regularization: 2 (Ridge) ( Prediction results obtained on fMRI data (see text) for the multi-class classification setting.  ... 
arXiv:1105.0363v2 fatcat:mkr26c2epncwrdi7ol267hmhue

Empirical Asymmetric Selective Transfer in Multi-objective Decision Trees [chapter]

Beau Piccart, Jan Struyf, Hendrik Blockeel
2008 Lecture Notes in Computer Science  
Applied to decision trees, EAST outperforms single-target decision trees, multi-target decision trees, and multi-target decision trees with target clustering.  ...  Two approaches have been used in this setting: (a) build a separate single-target model for each target variable, and (b) build a multi-target model that predicts all targets simultaneously; the latter  ...  Džeroski and B.Ženko for providing the datasets. This research utilizes the high performance computational resources provided by the K.U. Leuven (http://ludit.kuleuven.be/hpc).  ... 
doi:10.1007/978-3-540-88411-8_9 fatcat:liwnsb7ktfhqxon22ahsbvmodu

Disease prediction in big data healthcare using extended convolutional neural network techniques

Asadi Srinivasulu, Asadi Pushpa
2020 International Journal of Advances in Applied Sciences  
To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized.  ...  In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes.  ...  to classify/predict class or target variables of future/new data with the help of decision rules or decision trees.  ... 
doi:10.11591/ijaas.v9.i2.pp85-92 fatcat:i2mjvtynpjajtfraafjt7tnr4i

Feature Ranking for Semi-supervised Learning [article]

Matej Petković, Sašo Džeroski, Dragi Kocev
2020 arXiv   pre-print
and multi-target regression).  ...  The feature rankings are learned in the context of classification and regression as well as in the context of structured output prediction (multi-label classification, hierarchical multi-label classification  ...  The computational experiments presented here were executed on a computing infrastructure from the Slovenian Grid (SLING) initiative, and we thank the administrators Barbara Krašovec and Janez Srakar for  ... 
arXiv:2008.03937v1 fatcat:7nipvmrnf5fyto6h24kpyprhh4

Discussion of: Treelets—An adaptive multi-scale basis for sparse unordered data

Catherine Tuglus, Mark J. van der Laan
2008 Annals of Applied Statistics  
A composite of clustering and traditional principal components analysis, treelets is an innovative method for multi-resolution analysis of unordered data.  ...  We would like to congratulate Lee, Nadler and Wasserman on their contribution to clustering and data reduction methods for high p and low n situations.  ...  cluster in each node, orders the clusters in each layer of the hierarchical tree based on the distance so that neighboring clusters are close to each other w.r.t. the specified dissimilarity, and it allows  ... 
doi:10.1214/08-aoas137f pmid:25478036 pmcid:PMC4251495 fatcat:7n6p6jwa3fabzhnelmu7wr2q5i

A supervised clustering approach for extracting predictive information from brain activation images

Vincent Michel, Evelyn Eger, Christine Keribin, Jean-Baptiste Poline, Bertrand Thirion
2010 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops  
Then we prune the tree in order to produce a parcellation (division of the image domain) such that parcel-based signal averages optimally predict the target information.  ...  Here we propose a solution that computes a reduced set of high-level features which compress the image information while retaining its informative parts: first, we introduce a hierarchical clustering of  ...  a cut of the tree, where the sub-trees created by the cut represent a region whose average signal is used for regression.  ... 
doi:10.1109/cvprw.2010.5543435 dblp:conf/cvpr/MichelEKPT10 fatcat:juw3lyqpzffbnmki27yzbz5l7e

Decision Tree Induction & Clustering Techniques In SAS Enterprise Miner, SPSS Clementine, And IBM Intelligent Miner A Comparative Analysis

Abdullah M. Al Ghoson
2011 International Journal of Management & Information Systems  
Decision tree induction and Clustering are two of the most prevalent data mining techniques used separately or together in many business applications.  ...  There are many criteria and factors to choose the most appropriate software for a particular organization.  ...  In addition, SAS Enterprise Miner provides two other multi-split algorithms for regression tree that split for numeric target variables, which are Reduction in Variance and F-Test.  ... 
doi:10.19030/ijmis.v14i3.841 fatcat:r3ynp27de5blnog66ysalwksd4

Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition

Dragi Kocev, Sašo Džeroski, Matt D. White, Graeme R. Newell, Peter Griffioen
2009 Ecological Modelling  
(multi-target approach, a multi-target regression tree).  ...  In order to lift the predictive performance, we also employ ensembles (bagging and random forests) of regression trees and multi-target regression trees.  ...  The multi-target regression trees are an instantiation of the predictive clustering trees (PCTs) framework proposed in (Blockeel et al., 1998) .  ... 
doi:10.1016/j.ecolmodel.2009.01.037 fatcat:zrefyp66tzei7l6gfppezaapsq
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