VMFS: A VIKOR-based multi-target feature selection

Hashemi, Amin; Dowlatshahi, Mohammad Bagher; Nezamabadi‐pour, Hossein

doi:10.1016/j.eswa.2021.115224

Cited by 54 publications

(14 citation statements)

References 41 publications

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“…The authors state in [8] that one of the best known greedy heuristic approaches is MIFS (Mutual Information Feature Selection [18]), together with the mRMR method (maximal Relevance Minimum Redundancy [19]). In the literature, these methods are usually proposed for single-label classification problems, i.e., the approach for multi-label classification problems is not widely used [20].…”

Section: Filter Feature Selection Methodsmentioning

confidence: 99%

“…Multi-target regression studies the problem in which the input variables are associated with a set (at least two) of continuous targets [21]. As stated in [22] and [20], there are two main approaches to model it: (i) transformation-based MTR (also known as problem transformation or binary transformation) and (ii) adaptation-based MTR. The former converts the MTR problem into multiple STRs.…”

Section: Multi-target Regressionmentioning

confidence: 99%

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Filter method-based feature selection process for unattributed-identity multi-target regression problem

García¹,

Santana²

2023

Preprint

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<p>In this paper, for the first time, a feature selection (FS) problem for an unattributed-identity multi-target regression (UIMTR) problem is presented. UIMTR is defi?ned as a multi-target regression problem where the set of target and predictor variables are undetermined, i.e., the identity of the variables is unattributed. Two forward selection ?filter-based mutual information sequential-methods are proposed. In particular, the proposed methods are multi-objective adaptations of the classical Mutual Information Maximization (MIM) and Maximum Relevance Minimum Redundancy (mRMR). The concept of "sentinel variable" is also introduced in this paper: any variable selected by the methods that, a posteriori, will be a predictor variable (its real-time data will be used by the models to predict the value of the target variables). To highlight the existence of this type of problems in the industry, and thus the need for this approach, a current problem of low voltage power grids is presented and modelled. In particular, the question of selecting a subset of smart meters ("sentinel smart meters") that serve as predictors of a certain electrical measurement for the rest of the smart meters in the grid. The empirical approach will be applied to voltage curves of smart meters for six different transformer substations. The results are evaluated from three perspectives: (i) the quality of the predictions, (ii) the stability of the methods and (iii) the execution time. In addition, the results are compared with three other methods, a purely empirical one proposed in the article (based on voltage patterns (VP)) and another two which are well-known in the literature: (a) RReliefF (Relief for regressions) and (b) Fisher Score.</p>

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Section: Filter Feature Selection Methodsmentioning

confidence: 99%

Section: Multi-target Regressionmentioning

confidence: 99%

Filter method-based feature selection process for unattributed-identity multi-target regression problem

García¹,

Santana²

2023

Preprint

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“…By rating their mutual information with the target variable, we may choose the features from the feature space. The advantage of mutual information over the different statistical techniques is that it can handle non-linear relationships between features and target variables ( Hashemi, Dowlatshahi & Nezamabadi-pour, 2021 ). Formally, the mutual information between two random variables X and Y is as follows:…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

Kamat

Sugandhi

Kumar

2021

PeerJ Computer Science

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Remaining Useful Life (RUL) estimation of rotating machinery based on their degradation data is vital for machine supervisors. Deep learning models are effective and popular methods for forecasting when rotating machinery such as bearings may malfunction and ultimately break down. During healthy functioning of the machinery, however, RUL is ill-defined. To address this issue, this study recommends using anomaly monitoring during both RUL estimator training and operation. Essential time-domain data is extracted from the raw bearing vibration data, and deep learning models are used to detect the onset of the anomaly. This further acts as a trigger for data-driven RUL estimation. The study employs an unsupervised clustering approach for anomaly trend analysis and a semi-supervised method for anomaly detection and RUL estimation. The novel combined deep learning-based anomaly-onset aware RUL estimation framework showed enhanced results on the benchmarked PRONOSTIA bearings dataset under non-varying operating conditions. The framework consisting of Autoencoder and Long Short Term Memory variants achieved an accuracy of over 90% in anomaly detection and RUL prediction. In the future, the framework can be deployed under varying operational situations using the transfer learning approach.

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“…Due to the high popularity and the difficulty of selecting suitable methodologies for solving multi-criteria problems, MCDA/MCDM techniques are frequently being developed. The most widely used classical approaches for solving MCDA/MCDM problems are Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [22,23], VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) [24,25], Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) [26], Elimination et Choix Traduisant la Realité (ELECTRE) [27], Analytic Hierarchy Process (AHP) [28], Analytic Network Process (ANP) [29], Multi-Objective Optimization Method by Ratio Analysis (MOORA) [30] and Multi-Attribute Utility Theory (MAUT) [31].…”

Section: Introductionmentioning

confidence: 99%

Decision Support in Selecting a Reliable Strategy for Sustainable Urban Transport Based on Laplacian Energy of T-Spherical Fuzzy Graphs

Devi

Kizielewicz²,

Guleria³

et al. 2022

Energies

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Sustainable transportation has a significant impact on factors related to urban development and economic development. Therefore, much research is being undertaken to select the best strategies to manage sustainable transportation. Transportation requires a carefully designed method to manage the development of mobility modes in terms of the pollution they produce or the use of renewable energy sources. However, due to numerous preferences of decision-makers and data uncertainty problems, it is challenging to select the optimal strategy. In this paper, we focus on creating a framework for determining the best strategy for sustainable transportation management. For this purpose, T-spherical fuzzy graphs will be used, which, together with the combination of Laplacian Energy, can accurately represent decision-makers’ preferences in an uncertain environment. Due to the lack of limitations of T-spherical fuzzy graphs and its numerous membership functions, decision-makers can decide which factor seems most important for selecting the optimal sustainable transportation strategy. Additionally, due to the applicability, the SFS TOPSIS approach has been used in this approach. The obtained results demonstrate the high performance of the proposed approach and the applicability of the approach in management and sustainable transport problems.

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VMFS: A VIKOR-based multi-target feature selection

Cited by 54 publications

References 41 publications

Filter method-based feature selection process for unattributed-identity multi-target regression problem

Filter method-based feature selection process for unattributed-identity multi-target regression problem

Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

Decision Support in Selecting a Reliable Strategy for Sustainable Urban Transport Based on Laplacian Energy of T-Spherical Fuzzy Graphs

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