Efficient voting prediction for pairwise multilabel classification

Mencía, Eneldo Loza; Park, Sang-Hyeun; Fürnkranz, Johannes

doi:10.1016/j.neucom.2009.11.024

Cited by 58 publications

(23 citation statements)

References 13 publications

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“…Finally, we are also working on expanding these results to other learning problems where binary decomposition techniques are applied. To that end, we have obtained very positive results for the calibrated ranking approach to multilabel classification (Mencía et al 2010), and have promising preliminary results for ranking problems (Park and Fürnkranz 2007b). a subset of non-incident classifiers of c i .…”

Section: Discussionmentioning

confidence: 89%

Efficient prediction algorithms for binary decomposition techniques

Park

Fürnkranz

2011

Data Min Knowl Disc

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Binary decomposition methods transform multiclass learning problems into a series of two-class learning problems that can be solved with simpler learning algorithms. As the number of such binary learning problems often grows super-linearly with the number of classes, we need efficient methods for computing the predictions. In this paper, we discuss an efficient algorithm that queries only a dynamically determined subset of the trained classifiers, but still predicts the same classes that would have been predicted if all classifiers had been queried. The algorithm is first derived for the simple case of pairwise classification, and then generalized to arbitrary pairwise decompositions of the learning problem in the form of ternary error-correcting output codes under a variety of different code designs and decoding strategies.

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Section: Discussionmentioning

confidence: 89%

Efficient prediction algorithms for binary decomposition techniques

Park

Fürnkranz

2011

Data Min Knowl Disc

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“…The performance of TSCCA and TSPCCA are compared with the CLR method with majority voting strategy for pairwise multi-label classification [10], QWeightedML algorithm [8], Multi-label k-NN (ML-kNN) [3], Classifier Chain method (CC) [7] and the Two Stage Voting Method (TSVM) [9]. The training and the testing of TSCCA and TSPCCA were performed using a custom developed application that uses the MULAN 1 library for the machine learning framework Weka [17].…”

Section: Methodsmentioning

confidence: 99%

“…At prediction time (when majority voting strategy is usually used), one will get a ranking over Q + 1 labels (the Q original labels plus the calibration label). Besides the majority voting that is usually used strategy in the prediction phase of the CLR algorithm, Mencia et al [8] propose another more effective voting algorithm named Quick Weighted Voting algorithm for multi-label classification(QWeightedML). In our previous work [9] we proposed two stage voting strategy that signifcantly improves the testing times of the CLR and QWeightedML methods.…”

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confidence: 99%

Two Stage Classifier Chain Architecture for efficient pair-wise multi-label learning

Gjorgjevikj

Madjarov

2011

2011 IEEE International Workshop on Machine Learning for Signal Processing

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A common approach for solving multi-label learning problems using problem-transformation methods and dichotomizing classifiers is the pair-wise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming, especially in learning problems with large number of labels. To tackle this problem we propose a Two Stage Classifier Chain Architecture (TSCCA) for efficient pair-wise multi-label learning. Six different real-world datasets were used to evaluate the performance of the TSCCA. The performance of the architecture was compared with six methods for multi-label learning and the results suggest that the TSCCA outperforms the concurrent algorithms in terms of predictive accuracy. In terms of testing speed TSCCA shows better performance comparing to the pair-wise methods for multi-label learning.

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“…These methods usually belong to the group of algorithm adaptation methods. Other proposed methods, based on problem transformation, use base classifiers with higher computational efficiency, such as Naive Bayes [7] [15], the one-layer perceptron [9], etc., in order to reduce the computational complexity.…”

Section: The Landscape Of Mlc Approachesmentioning

confidence: 99%

Hybrid Decision Tree Architecture Utilizing Local SVMs for Multi-Label Classification

Madjarov

Gjorgjevikj

2012

Lecture Notes in Computer Science

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Abstract. Multi-label classification (MLC) problems abound in many areas, including text categorization, protein function classification, and semantic annotation of multimedia. Issues that severely limit the applicability of many current machine learning approaches to MLC are the large-scale problem and the high dimensionality of the label space, which have a strong impact on the computational complexity of learning. These problems are especially pronounced for approaches that transform MLC problems into a set of binary classification problems for which SVMs are used. On the other hand, the most efficient approaches to MLC, based on decision trees, have clearly lower predictive performance. We propose a hybrid decision tree architecture that utilizes local SVMs for efficient multi-label classification. We build decision trees for MLC, where the leaves do not give multi-label predictions directly, but rather contain SVM-based classifiers giving multi-label predictions. A binary relevance architecture is employed in each leaf, where a binary SVM classifier is built for each of the labels relevant to that particular leaf. We use several real-world datasets to evaluate the proposed method and its competition. Our hybrid approach on almost every classification problem outperforms the predictive performances of SVM-based approaches while its computational efficiency is significantly improved as a result of the integrated decision tree.

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Efficient voting prediction for pairwise multilabel classification

Cited by 58 publications

References 13 publications

Efficient prediction algorithms for binary decomposition techniques

Efficient prediction algorithms for binary decomposition techniques

Two Stage Classifier Chain Architecture for efficient pair-wise multi-label learning

Hybrid Decision Tree Architecture Utilizing Local SVMs for Multi-Label Classification

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