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Label Ranking Forests

Cláudio Rebelo de Sá, Carlos Soares, Arno Knobbe, Paulo Cortez
2016 Expert systems  
In this work, we propose an ensemble of decision trees for Label Ranking, based on Random Forests, which we refer to as Label Ranking Forests (LRF).  ...  The algorithms that have been developed/adapted to treat rankings of a fixed set of labels as the target object, include several different types of decision trees (DT).  ...  Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.  ... 
doi:10.1111/exsy.12166 fatcat:clp7doupijdybl7jflmvfw2i2a

Case-Based Label Ranking [chapter]

Klaus Brinker, Eyke Hüllermeier
2006 Lecture Notes in Computer Science  
Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels.  ...  It exhibits the appealing property of transparency and is based on an aggregation model which allows to incorporate a broad class of pairwise loss functions on label ranking.  ...  These algorithms defer processing the training data until an estimation for a new instance is requested, a property distinguishing this class of learning methods from typical model-based approaches.  ... 
doi:10.1007/11871842_53 fatcat:zcms462hgrezva6wsuao672bgu

Mining Association Rules for Label Ranking [chapter]

Cláudio Rebelo de Sá, Carlos Soares, Alípio Mário Jorge, Paulo Azevedo, Joaquim Costa
2011 Lecture Notes in Computer Science  
Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods.  ...  In this paper, we continue this line of work by proposing an adaptation of association rules for label ranking based on the APRIORI algorithm.  ...  Acknowledgment This work was partially supported by FCT project Rank! (PTDC/EIA/81178/2006) and Palco AdI project Palco3.0 financed by QREN and Fundo Europeu de Desenvolvimento Regional (FEDER).  ... 
doi:10.1007/978-3-642-20847-8_36 fatcat:eldzwzflvrakjpwfxdrnusfsze

Multi Label Ranking Based on Positive Pairwise Correlations Among Labels

Raed Alazaidah, Farzana Ahmad, Mohamad Mohsin
2019 ˜The œinternational Arab journal of information technology  
The first objective is to propose a new multi-label ranking algorithm based on the positive pairwise correlations among labels, while the second objective aims to propose new simple PTMs that are based  ...  on labels correlations, and not based on labels frequency as in conventional PTMs.  ...  To classify a new instance, all the binary models are invoked, and a ranking is obtained by counting the votes for each label.  ... 
doi:10.34028/iajit/17/4/2 fatcat:ymjzhgc4ejczpo3ycg2oa5etme

Improving Pairwise Ranking for Multi-label Image Classification [article]

Yuncheng Li, Yale Song, Jiebo Luo
2017 arXiv   pre-print
In this work, we propose two techniques to improve pairwise ranking based multi-label image classification: (1) we propose a novel loss function for pairwise ranking, which is smooth everywhere and thus  ...  Furthermore, they employ simple heuristics, such as top-k or thresholding, to determine which labels to include in the output from a ranked list of labels, which limits their use in the real-world setting  ...  would like to explore ways to leverage the distinctive properties of multi-label problem, such as label dependency, label sparsity, and missing labels.  ... 
arXiv:1704.03135v3 fatcat:gfg7enxv6rhk7fk4wl2vlxqvwu

Improving Label Ranking Ensembles using Boosting Techniques [article]

Lihi Dery, Erez Shmueli
2020 arXiv   pre-print
Label ranking is a prediction task which deals with learning a mapping between an instance and a ranking (i.e., order) of labels from a finite set, representing their relevance to the instance.  ...  In this paper, we propose a boosting algorithm which was specifically designed for label ranking tasks.  ...  For example, the Stacking approach [Wolpert, 1992] can be extended to support label ranking tasks, by developing a meta label ranker that receives the output rankings of simple models as input attributes  ... 
arXiv:2001.07744v1 fatcat:lclrjzmouzdgtg7n7n5jgloabe

Supervised clustering of label ranking data using label preference information

Mihajlo Grbovic, Nemanja Djuric, Shengbo Guo, Slobodan Vucetic
2013 Machine Learning  
The RankTree principled approach is based on a Ranking Tree algorithm previously proposed for label ranking prediction.  ...  In the third baseline, clustering is applied on a new feature space consisting of both features and label rankings, followed by mapping back to the original feature and ranking space.  ...  on the data set properties; ranking of movie suggestions for new members of a movie website based on user features; determining an order of questions in a survey for a specific user based on respondent's  ... 
doi:10.1007/s10994-013-5374-3 fatcat:jiadjfzxufhtjkeasypz3iduzm

Supervised Clustering of Label Ranking Data [chapter]

Mihajlo Grbovic, Nemanja Djuric, Slobodan Vucetic
2012 Proceedings of the 2012 SIAM International Conference on Data Mining  
The proposed algorithms were empirically evaluated on synthetic and reallife label ranking data. The PL-based method was superior to the heuristically-based supervised clustering approaches.  ...  Cluster membership and ranking for a new instance is determined by membership of its nearest prototype.  ...  The goal is to predict how would a new customer rank 10 sushis based on his/her features.  ... 
doi:10.1137/1.9781611972825.9 dblp:conf/sdm/GrbovicDV12 fatcat:6mos4tpxzfbz5ohzrftggaqxgq

Node ranking in labeled directed graphs

Krishna P. Chitrapura, Srinivas R. Kashyap
2004 Proceedings of the Thirteenth ACM conference on Information and knowledge management - CIKM '04  
Given an arbitrary directed graph with edge and node labels, we present a new flow-based model and an efficient method to dynamically rank the nodes of this graph with respect to any of the original labels  ...  Ranking documents for a given query in a hyperlinked document set and ranking of authors/articles for a given topic in a citation database are some typical applications of our method.  ...  Our Contributions We propose a new flow-based model for ranking documents biased by edge and node labels; and an efficient method for dynamically ranking for any given label.  ... 
doi:10.1145/1031171.1031281 dblp:conf/cikm/ChitrapuraK04 fatcat:2b2pcewp4zflpmgf4pr6ak2ggi

Heuristic Search for Rank Aggregation with Application to Label Ranking [article]

Yangming Zhou and Jin-Kao Hao and Zhen Li and Fred Glover
2022 arXiv   pre-print
To demonstrate its practical usefulness, the algorithm is applied to label ranking, which is an important machine learning task.  ...  The algorithm features a semantic crossover based on concordant pairs and a late acceptance local search reinforced by an efficient incremental evaluation technique.  ...  The Borda procedure uses a well-established voting rule in social choice theory.  ... 
arXiv:2201.03893v1 fatcat:62h2zhcnzjecbnna35rzqa4rjy

Ranking annotators for crowdsourced labeling tasks

Vikas C. Raykar, Shipeng Yu
2011 Neural Information Processing Systems  
In this paper we formalize the notion of a spammer and define a score which can be used to rank the annotators-with the spammers having a score close to zero and the good annotators having a high score  ...  Various methods have been proposed to estimate the consensus labels by correcting for the bias of annotators with different kinds of expertise.  ...  While the good annotators are ranked high by both methods the accuracy score gives a low rank to the malicious annotators. Accuracy does not capture the notion of a spammer.  ... 
dblp:conf/nips/RaykarY11 fatcat:oi53v4z67bdfndze5xjl4tby6u

Learning Low-Rank Label Correlations for Multi-label Classification with Missing Labels

Linli Xu, Zhen Wang, Zefan Shen, Yubo Wang, Enhong Chen
2014 2014 IEEE International Conference on Data Mining  
Specifically, a low rank structure is adopted to capture the complex correlations among labels.  ...  In addition, we incorporate a supplementary label matrix which augments the possibly incomplete label matrix by exploiting the label correlations.  ...  multi-label classification method is not only measured from a classification perspective, but also measured from a label ranking perspective.  ... 
doi:10.1109/icdm.2014.125 dblp:conf/icdm/XuWSWC14 fatcat:udnzyuecqnffrf3tdaj7gmxsze

Label ranking by learning pairwise preferences

Eyke Hüllermeier, Johannes Fürnkranz, Weiwei Cheng, Klaus Brinker
2008 Artificial Intelligence  
We compare RPC to existing label ranking methods, which are based on scoring individual labels instead of comparing pairs of labels.  ...  This work focuses on a particular learning scenario called label ranking, where the problem is to learn a mapping from instances to rankings over a finite number of labels.  ...  The goal is to learn to predict a total order, a ranking, of all possible labels for a new training example.  ... 
doi:10.1016/j.artint.2008.08.002 fatcat:rdikg7yzcbbuvgvuee4wbsbaou

Session-Aware Query Auto-completion using Extreme Multi-label Ranking [article]

Nishant Yadav, Rajat Sen, Daniel N. Hill, Arya Mazumdar, Inderjit S. Dhillon
2020 arXiv   pre-print
Another solution is to pre-compute a relatively small subset of relevant queries for common prefixes and rank them based on the context.  ...  In this paper, we provide a solution to this problem: we take the novel approach of modeling session-aware query auto-completion as an eXtreme Multi-Label Ranking (XMR) problem where the input is the previous  ...  . • We propose a set of label indexing methods for tree based XMR methods which are amenable to the QAC problem.  ... 
arXiv:2012.07654v1 fatcat:4r5ugi5kmfhy7krdvqjuab2vra

Multi-interval Discretization of Continuous Attributes for Label Ranking [chapter]

Cláudio Rebelo de Sá, Carlos Soares, Arno Knobbe, Paulo Azevedo, Alípio Mário Jorge
2013 Lecture Notes in Computer Science  
Label Ranking (LR) problems, such as predicting rankings of financial analysts, are becoming increasingly important in data mining.  ...  As a make-shift solution, one could consider conventional discretization methods used in classification, by simply treating each unique ranking as a separate class.  ...  Association Rules for Label Ranking Label Ranking Association Rules (LRAR) [7] are a straightforward adaptation of class Association Rules (CAR): A → π where A ⊆ desc (X) and π ∈ Ω.  ... 
doi:10.1007/978-3-642-40897-7_11 fatcat:b2cit6boazdwli2c5vffmhk2ae
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