Towards Multi-label Feature Selection by Instance and Label Selections

Mansouri, Dou El Kefel; Benabdeslem, Khalid

doi:10.1007/978-3-030-75765-6_19

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…There are two categories of dimensionality reduction approach: the variable selection approaches, which consist of electing a descriptor sample from the global variables set; and the variable transformation approaches, which consist of reconstructing a new descriptor set based on the similarity characteristics of the initial variables. The first method category generates a loss of useful information (partial data exploitation), which makes it suboptimal compared to the second [28,29].…”

Section: Study Motivations and Innovationsmentioning

confidence: 99%

“…This study fits into the evidence theory context, which takes into consideration ambiguity and vagueness, credibility and the conflict of different independent proofs, and then produces a certain and a confident result. A number of previous works have studied DS fusion, whether to interpret the relationship between different data sources (DGA [14], Ratio methods [28], etc. ), or the merging of several discriminator outputs [1,7,31,32], and have highlighted the theory contribution in the PTD problem.…”

Section: Study Motivations and Innovationsmentioning

confidence: 99%

“…The last set of descriptors is expressed as a percentage of three key %CH 4 , %C 2 H 2 , and %C 2 H 4 , relative to their total concentration (TS): (CH 4 , C 2 H 2 , C 2 H 4 ). The reader is invited to see reference [28] for more details on the graphing approach via Duval's Triangle 1.…”

Section: Iec Ratios (Iec-r)mentioning

confidence: 99%

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Using Generic Direct M-SVM Model Improved by Kohonen Map and Dempster–Shafer Theory to Enhance Power Transformers Diagnostic

Hendel,

Meghnefi,

Senoussaoui

et al. 2023

Sustainability

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Many power transformers throughout the world are nearing or have gone beyond their theoretical design life. Since these important assets represent approximately 60% of the cost of the substation, monitoring their condition is necessary. Condition monitoring helps in the decision to perform timely maintenance, to replace equipment or extend its life after evaluating if it is degraded. The challenge is to prolong its residual life as much as possible. Dissolved Gas Analysis (DGA) is a well-established strategy to warn of fault onset and to monitor the transformer’s status. This paper proposes a new intelligent system based on DGA; the aim being, on the one hand, to overcome the conventional method weaknesses; and, on the other hand, to improve the transformer diagnosis efficiency by using a four-step powerful artificial intelligence method. (1) Six descriptor sets were built and then improved by the proposed feature reduction approach. Indeed, these six sets are combined and presented to a Kohonen map (KSOM), to cluster the similar descriptors. An averaging process was then applied to the grouped data, to reduce feature dimensionality and to preserve the complete information. (2) For the first time, four direct Multiclass Support Vector Machines (M-SVM) were introduced on the Generic Model basis; each one received the KSOM outputs. (3) Dempster–Shafer fusion was applied to the nine membership probabilities returned by the four M-SVM, to improve the accuracy and to support decision making. (4) An output post-processing approach was suggested to overcome the contradictory evidence problem. The achieved AUROC and sensitivity average percentages of 98.78–95.19% (p-value < 0.001), respectively, highlight the remarkable proposed system performance, bringing a new insight to DGA analysis.

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Section: Study Motivations and Innovationsmentioning

confidence: 99%

Section: Study Motivations and Innovationsmentioning

confidence: 99%

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Using Generic Direct M-SVM Model Improved by Kohonen Map and Dempster–Shafer Theory to Enhance Power Transformers Diagnostic

Hendel,

Meghnefi,

Senoussaoui

et al. 2023

Sustainability

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Reducing the Label Space a Predefined Ratio for a More Efficient Multilabel Classification

Moyano¹,

Luna²,

Ventura³

2022

IEEE Access

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The multi-label classification task has been widely used to solve problems where each of the instances may be related not only to one class but to many of them simultaneously. Many of these problems usually comprise a high number of labels in the output space, so learning a predictive model from such datasets may turn into a challenging task since the computational complexity of most algorithms depends on the number of labels. In this paper, we propose a methodology to reduce the label space a predefined ratio of labels, aiming to improve the runtime of the multi-label classification algorithms. Obviously, such reduction should be done without producing a significant drop in their final predictive performance. The experimental analysis carried out over 25 well-known multi-label datasets, demonstrates a drastic reduction in the runtime. Besides, it is statistically proven that reducing 20% the number of labels does not lead to a decrease in the predictive performance of the multi-label algorithms using four well-known evaluation measures. Even more, in many cases, although reductions of up to 50% of the output space are made, the predictive performance of the algorithms is not significantly different from using the whole set of labels.

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