Multi-label Classification without the Multi-label Cost

Zhang, Xiatian; Quan, Yuan; Zhao, Shiwan; Wang, Fan; Zheng, Wentao; Wang, Zhong

doi:10.1137/1.9781611972801.68

Cited by 34 publications

(24 citation statements)

References 20 publications

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“…The primary motivation and the advantage of focussing on this variant is that it is highly scalable since, measures such as IG or GG do not have to be computed at every node in the tree. In addition, the prediction accuracy of this model is shown to be quite good in practice [Liu et al 2005;Fan et al 2003;Zhang and Fan 2008;Zhang et al 2010].…”

Section: Introductionmentioning

confidence: 78%

Bounds on the moments for an ensemble of random decision trees

Dhurandhar¹

2014

Knowl Inf Syst

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An ensemble of random decision trees is a popular classification technique, especially known for its ability to scale to large domains. In this paper, we provide an efficient strategy to compute bounds on the moments of the generalization error computed over all datasets of a particular size drawn from an underlying distribution, for this classification technique. Being able to estimate these moments can help us gain insights into the performance of this model. As we will see in the experimental section these bounds tend to be significantly tighter than the state-of-the-art Breiman's bounds based on strength and correlation and hence, more useful in practice.

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Section: Introductionmentioning

confidence: 78%

Bounds on the moments for an ensemble of random decision trees

Dhurandhar¹

2014

Knowl Inf Syst

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“…Furthermore, we train an additional recommender on the joint feature set, using Random Decision Trees (RDTs) [4]. RDTs generate k 1 decision trees with maximal depth k 2 and random attribute tests at the inner nodes.…”

Section: Recommender Strategiesmentioning

confidence: 99%

A Hybrid Multi-strategy Recommender System Using Linked Open Data

Ristoski

Mencía

Paulheim

2014

Communications in Computer and Information Science

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Abstract. In this paper, we discuss the development of a hybrid multistrategy book recommendation system using Linked Open Data. Our approach builds on training individual base recommenders and using global popularity scores as generic recommenders. The results of the individual recommenders are combined using stacking regression and rank aggregation. We show that this approach delivers very good results in different recommendation settings and also allows for incorporating diversity of recommendations.Keywords: Linked Open Data, Hybrid Recommender Systems, Stacking Overall ApproachWe propose a hybrid, multi-strategy approach that combines the results of different base recommenders and generic recommenders into a final recommendation. A base recommender is an individual collaborative or content based recommender system, whereas a generic recommender makes a recommendation solely on some global popularity score, which is the same for all users. For base recommenders, we use two collaborative filtering strategies (item and user based), as well as different content-based strategies exploiting various feature sets created from DBpedia 1 . Our results in the three tasks of the LOD-enabled Recommender Systems Challenge 2014 2 demonstrate the effectiveness of our approach. Generic RecommendersWe use different generic recommenders in our approach. First, the RDF Book Mashup dataset 3 provides the average score assigned to a book on Amazon. Furthermore, DBpedia provides the number of ingoing links to the Wikipedia 1 http://dbpedia.org 2 75,559 numeric ratings on 6,166 books (from 0-5, Task 1) and 72,372 binary ratings on 6733 books (Tasks 2 and 3), resp., from 6,181 users for training, and evaluation on 65,560 and 67,990 unknown ratings, resp. See http://challenges.2014.eswcconferences.org/index.php/RecSys for details. 3

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“…We follow a similar strategy to design our classification model with many random tree ensemble methods in either single-label stream classification [1] or multi-label batch classification [38]. But we designed our random tree model to address the special properties in multi-label stream classification problem.…”

Section: Related Workmentioning

confidence: 99%

“…Streaming Random Forest [1] builds streaming decision trees by extending Breiman's Random Forests [29], which is focused on single-label data stream classification problems. Random Decision Tree [11] has also be extended to multi-label batch classification problems in [38].…”

Section: Related Workmentioning

confidence: 99%

“…Update the child node with TreeUpdate(x, y, t); Our approach starts from a mainstream idea for learning label relevance and regression that builds an ensemble of multiple random trees [1], [38]. As its name implies, this idea is about training a set of random trees where tree nodes are built by randomly selected testing variables and cutting values.…”

Section: Outputmentioning

confidence: 99%

See 1 more Smart Citation

An Ensemble-based Approach to Fast Classification of Multi-label Data Streams

Kong¹,

Yu²

2011

Proceedings of the 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing

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Abstract-Network operators are continuously confronted with online events, such as online messages, blog updates, etc. Due to the huge volume of these events and the fast changes of the topics, it is critical to manage them promptly and effectively. There have been many softwares and algorithms developed to conduct automatic classification over these stream data. Conventional approaches focus on single-label scenarios, where each event can only be tagged with one label. However, in many stream data, each event can be tagged with more than one labels. Effective stream classification systems should be able to consider the unique properties of multi-label stream data, such as large data volumes, label correlations and concept drifts. To address these challenges, in this paper, we propose an efficient and effective method for multi-label stream classification based on an ensemble of fading random trees. The proposed model can efficiently process high-speed multi-label stream data with concept drifts. Empirical studies on real-world tasks demonstrate that our method can maintain a high accuracy in multi-label stream classification, while providing a very efficient solution to the task.

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Multi-label Classification without the Multi-label Cost

Cited by 34 publications

References 20 publications

Bounds on the moments for an ensemble of random decision trees

Bounds on the moments for an ensemble of random decision trees

A Hybrid Multi-strategy Recommender System Using Linked Open Data

An Ensemble-based Approach to Fast Classification of Multi-label Data Streams

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