Multilayer Perceptron for Label Ranking

Ribeiro, Geraldina; Duivesteijn, Wouter; Soares, Carlos; Knobbe, Arno

doi:10.1007/978-3-642-33266-1_4

Cited by 25 publications

(33 citation statements)

References 8 publications

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“…2) Restricted preferences results: Table III summarizes PNN ranking performance of strict label ranking datasets by learning rate and the total number of hidden neurons. The results are compared with the four methods for label ranking; supervised clustering [27], supervised decision tree [28], multi-layer perceptron label ranking [29] and label ranking tree forest (LRT) [35]. The comparison selects only the best approach for each method.…”

Section: B Resultsmentioning

confidence: 99%

“…The similarity methods introduced similarity measures by minimizing the distance instead of maximizing the probability of label values, i.e., Naive Bayes [34] and association rules [13]. The ensemble methods adapt the existing multi-class classifiers to rank multiple labels, i.e., multi-layer perceptron for label ranking (MLP-LR) [29] and Rank-net [7]. The instance-based decision tree was introduced by Cheng and Hüllermeier to rank the labels based on predictive probability models of a decision tree [28].…”

Section: Introductionmentioning

confidence: 99%

“…Rank-net uses Gradient descent and probabilistic ranking cost function for each object pairs. Multi-layer perceptron (MLP) for label ranking [29] employs NN architecture using sigmoid activation function to calculate the error between the actual and expected values for the output labels. However, it does not predict indifference preference relations between labels and unable to represent all labels as output neurons.…”

Section: Introductionmentioning

confidence: 99%

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Preference Neural Network

Elgharabawy¹,

Parsad²,

Lin³

2019

Preprint

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This paper proposes a preference neural network (PNN) to address the problem of indifference preferences orders with new activation function. PNN also solves the Multi-label ranking problem, where labels may have indifference preference orders or subgroups are equally ranked. PNN follows a multi-layer feedforward architecture with fully connected neurons. Each neuron contains a novel smooth stairstep activation function based on the number of preference orders. PNN inputs represent data features and output neurons represent label indexes. The proposed PNN is evaluated using new preference mining dataset that contains repeated label values which have not experimented before. PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency.

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Section: B Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Preference Neural Network

Elgharabawy¹,

Parsad²,

Lin³

2019

Preprint

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“…With the advancement of artificial intelligence technologies, artificial neural networks and deep learning models for ranking data have been proposed. Ribeiro, Duivesteijn, Soares, and Knobbe (2012) proposed a multilayer perceptron with the network weights determined by the error back propagation learning algorithm. C. Burges et al (2005) introduced RankNet, which has a deep neural network architecture and proposed to use gradient descent to perform preference learning.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Analysis of ranking data

2019

WIREs Computational Stats

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Ranking is one of the simple and efficient data collection techniques to understand individuals' perception and preferences for some items such as products, people, and species. Ranking data are frequently collected when individuals are asked to rank a set of items according to a certain preference criterion. Over the years, many statistical models and methods have been developed for analyzing ranking data. This paper will give a literature review of these models and methods and present the recent advances of the analysis of ranking data. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Nonparametric Methods

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“…Label Ranking (LR) is an increasingly popular topic in the machine learning literature (Ribeiro, Duivesteijn, Soares, & Knobbe, 2012;de Sá, Soares, Jorge, Azevedo, & da Costa, 2011;Cheng & Hüllermeier, 2011;Cheng, Hüllermeier, Waegeman, & Welker, 2012;Vembu & Gärtner, 2010). LR studies a problem of learning a mapping from instances to rankings over a finite number of predefined labels.…”

Section: Introductionmentioning

confidence: 99%

Label Ranking Forests

et al. 2016

Self Cite

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The problem of Label Ranking is receiving increasing attention from several research communities. The algorithms that have been developed/adapted to treat rankings of a fixed set of labels as the target object, including several different types of decision trees (DT). One DT‐based algorithm, which has been very successful in other tasks but which has not been adapted for label ranking is the Random Forests (RF) algorithm. RFs are an ensemble learning method that combines different trees obtained using different randomization techniques. 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). Two different algorithms that learn DT for label ranking are used to obtain the trees. We then compare and discuss the results of LRF with standalone decision tree approaches. The results indicate that the method is highly competitive.

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Multilayer Perceptron for Label Ranking

Cited by 25 publications

References 8 publications

Preference Neural Network

Preference Neural Network

Analysis of ranking data

Label Ranking Forests

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