Categorizing feature selection methods for multi-label classification

Pereira, Rafael B.; Plastino, Alexandre; Zadrozny, Bianca; Merschmann, Luiz Henrique de Campos

doi:10.1007/s10462-016-9516-4

Cited by 145 publications

(48 citation statements)

References 54 publications

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“…The distribution of works in the field of Text Mining (more than 11,000 works in 4 years) and Tech Mining (400 works in 4 years) is also quite interesting. The largest contribution to Text Mining is made by American scientists and is mainly focused on the development of these methods in relation to social media [18,19]. And Tech Mining is more developed by Chinese scientists for the tasks of scientific and technological forecasting [20][21][22].…”

Section: Energy Technology Forecastingmentioning

confidence: 99%

Technology of Application of Software Tools for Energy Technology Forecasting

Kopaygorodsky¹

2019

Proceedings of the 21st International Workshop on Computer Science and Information Technologies (CSIT 2019)

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In this paper an approach to the collection and analysis of information for the task of scientific and technological forecasting is considered. The approach is based on the use of ontologies, Tech Mining and Data Analysis methods. The developed approach is supported by the distributed intellectual information system created by the author. The technology of using tools for solving the problem is presented.

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Section: Energy Technology Forecastingmentioning

confidence: 99%

Technology of Application of Software Tools for Energy Technology Forecasting

Kopaygorodsky¹

2019

Proceedings of the 21st International Workshop on Computer Science and Information Technologies (CSIT 2019)

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“…They illustrate the necessity of providing a complete taxonomy for ML-FS in this work. Another paper by Pereira, Plastino, Zadrozny, and Merschmann (2016) reviews ML-FS methods, which provides a good categorization of ML-FS methods. To the best of our knowledge, there is no more review paper in ML-FS task.…”

Section: This Article Is Categorized Undermentioning

confidence: 99%

Multilabel feature selection: A comprehensive review and guiding experiments

Kashef

Nezamabadi‐pour

Nikpour

2018

WIREs Data Min & Knowl

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Feature selection has been an important issue in machine learning and data mining, and is unavoidable when confronting with high‐dimensional data. With the advent of multilabel (ML) datasets and their vast applications, feature selection methods have been developed for dimensionality reduction and improvement of the classification performance. In this work, we provide a comprehensive review of the existing multilabel feature selection (ML‐FS) methods, and categorize these methods based on different perspectives. As feature selection and data classification are closely related to each other, we provide a review on ML learning algorithms as well. Also, to facilitate research in this field, a section is provided for setup and benchmarking that presents evaluation measures, standard datasets, and existing software for ML data. At the end of this survey, we discuss some challenges and open problems in this field that can be pursued by researchers in future. WIREs Data Mining Knowl Discov 2018, 8:e1240. doi: 10.1002/widm.1240 This article is categorized under: Technologies > Data Preprocessing

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“…As different attributes are correlated with different labels, a local solution, also called embedded (Pereira, Plastino, Zadrozny, & Merschmann, 2018), for the transformation strategies is employed. Before inducing the internal models, the Relief algorithm (Robnik-Sikonja & Kononenko, 2003) is used to select the most relevant attributes for each label.…”

Section: Attribute Selectionmentioning

confidence: 99%

“…Despite its inability to completely eliminate them, it is a good indicative that investing in similar techniques can mitigate the label prediction problems. Thus, the investigation of other MLC techniques for data oversampling Charte et al, 2017) and attribute selection (Pereira et al, 2018) as solutions to the MLP and WLP problems are suggested for future work. Results showing the effects of using preprocessing techniques.…”

Section: Attribute Selection and Data Oversamplingmentioning

confidence: 99%

Enhancing multilabel classification for food truck recommendation

2018

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Food trucks are a widely popular fast food restaurant alternative, whose differentiating factor is their proximity to customers. Their popularity has stimulated the expansion of available options, which now includes several different types of cuisines, consequently making the choice by customers a challenging issue. From data obtained via a market research, in which hundreds of participants provided their food truck preferences, this paper focuses on the problem of food truck recommendation using a multilabel approach. In particular, it investigates how to improve the recommendation task regarding a previous work, where some labels have never been predicted. In order to address this problem, different alternatives were investigated. One of these alternatives, the Ensemble of Single Label, proposed in this paper, was able to reduce it. Despite its simplicity, good predictive results were obtained when they were used in the investigated task. Among other benefits, all labels were correctly predicted at least for few instances.

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Categorizing feature selection methods for multi-label classification

Cited by 145 publications

References 54 publications

Technology of Application of Software Tools for Energy Technology Forecasting

Technology of Application of Software Tools for Energy Technology Forecasting

Multilabel feature selection: A comprehensive review and guiding experiments

Enhancing multilabel classification for food truck recommendation

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