rNPBST: An R Package Covering Non-parametric and Bayesian Statistical Tests

Carrasco, Jacinto; García, Salvador; Rueda, María del Mar; Herrera, Francisco

doi:10.1007/978-3-319-59650-1_24

Cited by 27 publications

(16 citation statements)

References 12 publications

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“…The location of most of the distribution in these sectors indicates the final decision: the superiority of algorithm L, statistical equivalence and the superiority of algorithm R, respectively. KEEL package [69] has been used to compute the Wilcoxon test and the R package rNPBST [70] was used to extract the graphical representations of the Bayesian Sign tests analyzed in the following empirical studies. The Rope limit parameter used to represent the Bayesian Sign test is 0.0001.…”

Section: Results and Analysismentioning

confidence: 99%

ProLSFEO-LDL: Prototype Selection and Label- Specific Feature Evolutionary Optimization for Label Distribution Learning

2020

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Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life machine learning applications. In LDL problems, instance-based algorithms and particularly the adapted version of the k-nearest neighbors method for LDL (AA-kNN) has proven to be very competitive, achieving acceptable results and allowing an explainable model. However, it suffers from several handicaps: it needs large storage requirements, it is not efficient predicting and presents a low tolerance to noise. The purpose of this paper is to mitigate these effects by adding a data reduction stage. The technique devised, called Prototype selection and Label-Specific Feature Evolutionary Optimization for LDL (ProLSFEO-LDL), is a novel method to simultaneously address the prototype selection and the label-specific feature selection pre-processing techniques. Both techniques pose a complex optimization problem with a huge search space. Therefore, we have proposed a search method based on evolutionary algorithms that allows us to obtain a solution to both problems in a reasonable time. The effectiveness of the proposed ProLSFEO-LDL method is verified on several real-world LDL datasets, showing significant improvements in comparison with using raw datasets.

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Section: Results and Analysismentioning

confidence: 99%

ProLSFEO-LDL: Prototype Selection and Label- Specific Feature Evolutionary Optimization for Label Distribution Learning

2020

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“…To further analyze these results, we applied the non-parametric BSR test [3]. According to this test three possibilities do exist for a given pairwise comparison of methods A and B: (scenario 1) A outperforms B, (scenario 2) both methods show the same performance, or (scenario 3) B outperforms A.…”

Section: Resultsmentioning

confidence: 99%

A New Weighted k-Nearest Neighbor Algorithm Based on Newton’s Gravitational Force

Aguilera

González

Montes-y-Gómez

et al. 2019

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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The kNN algorithm has three main advantages that make it appealing to the community: it is easy to understand, it regularly offers competitive performance and its structure can be easily tuning to adapting to the needs of researchers to achieve better results. One of the variations is weighting the instances based on their distance. In this paper we propose a weighting based on the Newton's gravitational force, so that a mass (or relevance) has to be assigned to each instance. We evaluated this idea in the kNN context over 13 benchmark data sets used for binary and multi-class classification experiments. Results in F1 score, statistically validated, suggest that our proposal outperforms the original version of kNN and is statistically competitive with the distance weighted kNN version as well.

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“…Therefore, while in the hypothesis statistical tests when null hypothesis is not rejected there is no information at all, bayesian tests provide useful information. In addition, they offer more robust results, as they are not as influenced by the number of observations as the previous ones [347].…”

Section: Dynamic Optimizationmentioning

confidence: 92%