Local sets for multi-label instance selection

Arnaiz‐González, Álvar; Díez-Pastor, José-Francisco; Rodrı́guez, Juan J.; García‐Osorio, César

doi:10.1016/j.asoc.2018.04.016

Cited by 25 publications

(9 citation statements)

References 33 publications

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“…The research conducted in [48] is also of interest to investigate the impact of instance selection on the underlying structure of a dataset by analyzing the distribution of sample types. However, as of today, instance selection has not been extensively researched in the domain of MLL and to date only few methods have been made available [26][27][28]. As for LDL, we have not been able to find any studies to date.…”

Section: Prototype Selection and Label-specific Feature Learningmentioning

confidence: 98%

“…A few methods are available to approach the instance selection in the MLL domain [26][27][28] but, to the best of our knowledge, there are no studies concerning instance or prototype selection reported in LDL. With regard to the feature selection, most of LDL algorithms are built on a simple feature space where all features are used to predict all the labels.…”

Section: Introductionmentioning

confidence: 99%

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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: Prototype Selection and Label-specific Feature Learningmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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

2020

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“…Local sets for multi-label instance selection [2], is primarily motivated by the concept of local sets given by Brighton and Mellish [25]. They proposed two techniques on the adaptation of local sets for multi-label data.…”

Section: Ml-knnmentioning

confidence: 99%

“…The performance of the sets is evaluated using some traditional and state-of -the-art techniques. These include MlkNN [4], BrkNN [3], Rakel [29], HOMER [24], IBLR [30], LAMLKNN [6], HDLSSm [2] and HDLSBo [2]. Most of them are based on lazy learning, while others are based on space partitioning.With the exception LAMLKNN, HDLSSm and HDLSSo, all approaches are available in mulan.…”

Section: ) Experimental Setupmentioning

confidence: 99%

Discriminative Adaptive Sets for Multi-Label Classification

2020

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Multi-label classification aims to associate multiple labels to a given data/object instance to better describe them. Multi-label data sets are common in a lot of emerging application areas like: Text/Multimedia classification, Bio-Informatics, Medical image annotations and Computer Vision to name a few. There is a growing interest in efficient and accurate multi-label classification. There are two major approaches to perform multi-label classification (i) problem transformation methods and (ii) algorithm adaptation methods. In algorithm adaptation, the traditional classification algorithms are modified to handle multi-label data sets. One classification algorithm which is often modified to do multi-label classification is k-nearest neighbor (kNN). k-nearest neighbor is popular due to its simplicity, easy to implement and seamlessly adaptability. Despite its merits it has several drawbacks like: sensitivity of noisy data, missing values and outliers; feature scaling and often becoming inaccurate for large overlapping solution space. In this paper, a modification to kNN method is suggested for multi-label classification with three improvement strategies (i) selection of local example w.r.t. unknown example-the motivation for this comes from the fact that local and relevant space is vital for the improvement in multi-label classification; (ii) Splitting the input space into multiple sub-spaces for optimal label estimation-the motivation is to estimate label accurately in the presence of noisy labels; And (iii) selection of labels using Mean Average Precision (MAP) estimates-here our motivation is to utilize the training data effectively to maximize the hidden distribution and optimal parameters for the method. The proposed method is implemented and compared with state-ofthe-art approaches based on kNN or similar approaches that effectively select and optimize relevant spaces for multi-label classification. Evaluation based on multiple metrics like Hamming loss, Precision/Recall and F-measure are used for evaluation. The suggested approach performed much better than the state-of-the-art on the datasets with strong label cardinalities.

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“…For instance, Multi-Editing Condensing Nearest Neighbor [25] first applies the Multi-Editing technique to reduce the amount of noise and then uses Condensing, mostly keeping those relevant prototypes. The Decremental Reduction Optimization Procedure [26] orders the prototypes according to the distance to their nearest neighbours and then, starting from the furthest ones, prototypes are removed as long as they do not affect the accuracy; the Iterative Case Filtering [27] bases its performance on the coverage and reachability premises to select a prototype subset that is able to maximize the accuracy; it has been recently extended to deal with multi-label classification [28]. In addition, Evolutionary Algorithms have also been adapted to perform PS [29,30].…”

Section: Common Extensions To This Technique Are the Repeated-editingmentioning

confidence: 99%

Extensions to rank-based prototype selection in k-Nearest Neighbour classification

Rico-Juan

Valero-Mas²,

Calvo-Zaragoza

2019

Applied Soft Computing

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The k-nearest neighbour rule is commonly considered for classification tasks given its straightforward implementation and good performance in many applications. However, its efficiency represents an obstacle in real-case scenarios because the classification requires computing a distance to every single prototype of the training set. Prototype Selection (PS) is a typical approach to alleviate this problem, which focuses on reducing the size of the training set by selecting the most interesting prototypes. In this context, rank methods have been postulated as a good solution: following some heuristics, these methods perform an ordering of the prototypes according to their relevance in the classification task, which is then used to select the most relevant ones. This work presents a significant improvement of existing rank methods by proposing two extensions: i) a greater robustness against noise at label level by considering the parameter 'k' of the classification in the selection process; and ii) a new parameter-free rule to select the prototypes once they have been ordered. The experiments performed in different scenarios and datasets demonstrate the goodness of these extensions. Also, it is empirically proved that the new full approach is competitive with respect to existing PS algorithms.

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Local sets for multi-label instance selection

Cited by 25 publications

References 33 publications

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

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

Discriminative Adaptive Sets for Multi-Label Classification

Extensions to rank-based prototype selection in k-Nearest Neighbour classification

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