Study of data transformation techniques for adapting single-label prototype selection algorithms to multi-label learning

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

doi:10.1016/j.eswa.2018.05.017

Cited by 15 publications

(4 citation statements)

References 43 publications

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“…This process continues following the randomly ordered chain sequence until the last label is predicted. 3) Label powerset (LP) multi-label classification algorithm is a simple but less popular of the problem transformation methods [30]. The MLC algorithm takes into consideration label correlations that may exist among the class labels.…”

Section: Multi-label Classification (Mlc) Modelsmentioning

confidence: 99%

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Multi-label classification approach for quranic verses labeling

Adeleke

Samsudin

Rahim

et al. 2021

IJEECS

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Machine learning involves the task of training systems to be able to make decisions without being explicitly programmed. Important among machine learning tasks is classification involving the process of training machines to make predictions from predefined labels. Classification is broadly categorized into three distinct groups: single-label (SL), multi-class, and multi-label (ML) classification. This research work presents an application of a multi-label classification (MLC) technique in automating Quranic verses labeling. MLC has been gaining attention in recent years. This is due to the increasing amount of works based on real-world classification problems of multi-label data. In traditional classification problems, patterns are associated with a single-label from a set of disjoint labels. However, in MLC, an instance of data is associated with a set of labels. In this paper, three standard <em>MLC</em> methods: <span>binary relevance (BR), classifier chain (CC), and label powerset (LP) algorithms are implemented with four baseline classifiers: support vector machine (SVM), naïve Bayes (NB), k-nearest neighbors (k-NN), and J48. The research methodology adopts the multi-label problem transformation (PT) approach. The results are validated using six conventional performance metrics. These include: hamming loss, accuracy, one error, micro-F1, macro-F1, and avg. precision. From the results, the classifiers effectively achieved above 70% accuracy mark. Overall, SVM achieved the best results with CC and LP algorithms.</span>

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Section: Multi-label Classification (Mlc) Modelsmentioning

confidence: 99%

“…For a new instance, the single-label classifier predicts the most likely label (which in return is a set of labels). The major setback with LP is high complexity [30] as a result of large number of possible label subsets combinations that could exist.…”

Section: Multi-label Classification (Mlc) Modelsmentioning

confidence: 99%

Multi-label classification approach for quranic verses labeling

Adeleke

Samsudin

Rahim

et al. 2021

IJEECS

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“…So in recent years, research has increasingly focused on adapting existing techniques to these new paradigms, or presenting new techniques to resolve these multi-label problems. Papers have been published on classification [9,10], regression [46,47], and pre-processing techniques [48,49,50].…”

Section: Multi-label and Multi-output Learningmentioning

confidence: 99%

Early and extremely early multi-label fault diagnosis in induction motors

et al. 2020

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“…Recently, adaptations of instance selection methods for multi-output classification problems were proposed [ 21 ]. A taxonomy and comparative study of instance selection methods for classification can be found in [ 2 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Evolutionary Instance Selection for Regression Tasks

Kordos

Łapa

2018

Entropy

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The purpose of instance selection is to reduce the data size while preserving as much useful information stored in the data as possible and detecting and removing the erroneous and redundant information. In this work, we analyze instance selection in regression tasks and apply the NSGA-II multi-objective evolutionary algorithm to direct the search for the optimal subset of the training dataset and the k-NN algorithm for evaluating the solutions during the selection process. A key advantage of the method is obtaining a pool of solutions situated on the Pareto front, where each of them is the best for certain RMSE-compression balance. We discuss different parameters of the process and their influence on the results and put special efforts to reducing the computational complexity of our approach. The experimental evaluation proves that the proposed method achieves good performance in terms of minimization of prediction error and minimization of dataset size.

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Study of data transformation techniques for adapting single-label prototype selection algorithms to multi-label learning

Cited by 15 publications

References 43 publications

Multi-label classification approach for quranic verses labeling

Multi-label classification approach for quranic verses labeling

Early and extremely early multi-label fault diagnosis in induction motors

Multi-Objective Evolutionary Instance Selection for Regression Tasks

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