A first approach towards the usage of classifiers’ performance to create fuzzy measures for ensembles of classifiers: a case study on highly imbalanced datasets

Uriz, Mikel; Paternain, Daniel; Bustince, Humberto; Galar, Mikel

doi:10.1109/fuzz-ieee.2018.8491440

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…Some of the literatures were on HIB data and the rest were on HIMC data. The related HIB data was from the data mining domain, and this data was grouped based on these algorithms: SR 0-1 LOSS [15], F-BFR [156], T-BFS [157], K-SUB [64], PSU [102], C4.5N [158], DWCE [159], ECSM [72], GP-COACH [160], Fuzzy [161], [162], OSPREY [163], Chi Method, CNN [164], WL-Norm SVM [165], EAIS + Fuzzy [166], GA + Fuzzy [167], US + Ensemble (Data Hardness) [15], Clustering + WS [66], DBE-DCR [73], EUBoost [168], GA-FS-GL [169], GSVM-RU [170], GA-GL+FRBC [171], K-Means + HFS [172], REPMAC k-Means +SVM + DT [173], SwitchingNED [70], SVM-US [174], B-BFS [67].…”

Section: A Related Study On Himc Data Frameworkmentioning

confidence: 99%

“…Nonetheless, the only research using DL strategy focused on data mining domain. Data Mining C4.5N [158], DWCE [159], ECSM [72], GP-COACH [160], Fuzzy [161], [162], OSPREY [163], Chi Method, CNN [164], WL-Norm SVM [165] Data Mining EAIS + Fuzzy [166], GA + Fuzzy [167], US + Ensemble (Data Hardness) [15], Clustering + WS [66], DBE-DCR [73], EUBoost [168], GA-FS-GL [169], GSVM-RU [170], GA-GL+FRBC [171], K-Means + HFS [172], REPMAC k-Means +SVM + DT [173], SwitchingNED [70], SVM-US [174], B-BFS [67] Data Mining NRA [175] Fraud Detection BERT [19] Malware Detection Ensemble + RF [68] Disease Prediction SSFS [176] Phishing Detection CNN [177], ECDL [178] Medical Imaging DS + ST [179] Hospital Admission…”

Section: A Related Study On Himc Data Frameworkmentioning

confidence: 99%

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Survey on Highly Imbalanced Multi-class Data

Hamid¹,

Yusoff²,

Mohamed³

2022

IJACSA

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Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic. Data distribution in machine learning has been an essential aspect in providing unbiased solutions. From the earliest literatures published on highly imbalanced data until recently, machine learning research has focused mostly on binary classification data problems. Research on highly imbalanced multi-class data is still greatly unexplored when the need for better analysis and predictions in handling Big Data is required. This study focuses on reviews related to the models or techniques in handling highly imbalanced multi-class data, along with their strengths and weaknesses and related domains. Furthermore, the paper uses the statistical method to explore a case study with a severely imbalanced dataset. This article aims to (1) understand the trend of highly imbalanced multi-class data through analysis of related literatures; (2) analyze the previous and current methods of handling highly imbalanced multi-class data; (3) construct a framework of highly imbalanced multi-class data. The chosen highly imbalanced multi-class dataset analysis will also be performed and adapted to the current methods or techniques in machine learning, followed by discussions on open challenges and the future direction of highly imbalanced multi-class data. Finally, for highly imbalanced multi-class data, this paper presents a novel framework. We hope this research can provide insights on the potential development of better methods or techniques to handle and manipulate highly imbalanced multiclass data.

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Section: A Related Study On Himc Data Frameworkmentioning

confidence: 99%

Section: A Related Study On Himc Data Frameworkmentioning

confidence: 99%

Survey on Highly Imbalanced Multi-class Data

Hamid¹,

Yusoff²,

Mohamed³

2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…1) Coalition-based performance measure: The coalitionbased performance (CPM) fuzzy measure learning algorithm was first given in [28] and later in [29]. Basically, the accuracy of every classifier and any combination of classifiers is collected (estimated in the training set).…”

Section: B Unsupervised Learning Of the Fuzzy Measurementioning

confidence: 99%

An Empirical Study on Supervised and Unsupervised Fuzzy Measure Construction Methods in Highly Imbalanced Classification

Uriz

Paternain

Bustince

et al. 2020

2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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The design of an ensemble of classifiers involves the definition of an aggregation mechanism that produces a single response obtained from the information provided by the classifiers. A specific aggregation methodology that has been studied in the literature is the use of fuzzy integrals, such as the Choquet or the Sugeno integral, where the associated fuzzy measure tries to represent the interaction existing between the classifiers of the ensemble. However, defining the big number of coefficients of a fuzzy measure is not a trivial task and therefore, many different algorithms have been proposed. These can be split into supervised and unsupervised, each class having different learning mechanisms and particularities. Since there is no clear knowledge about the correct method to be used, in this work we propose an experimental study for comparing the performance of eight different learning algorithms under the same framework of imbalanced dataset. Moreover, we also compare the specific fuzzy integral (Choquet or Sugeno) and their synergies with the different fuzzy measure construction methods.

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“…2) Uriz FM: Uriz et al [7] proposed a method which learns the FM from the classification accuracies of individual classifiers. This method shares key aspects of the motivation in respect to using the performance of individual classifiers within an ensemble classification framework with the approach put forward in this paper, nevertheless follows a different approach in the generation of the actual FM as detailed in Section III.…”

Section: A Fuzzy Measuresmentioning

confidence: 99%

“…In this paper, we develop one specific instance of an a priori FM: one which captures the quality of individual classifiers (and their combinations) in order to then enable a fusionbased ensemble classifier. While working on this paper, the authors became aware of recent work by Uriz et al [7] which introduces a FM based on the same principle (i.e. a FM based on sub-classifier performance) in the context of imbalanced classification problems and traditional FIs.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures

Agrawal

Wagner

Garibaldi

et al. 2019

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Aggregation operators are mathematical functions that enable the fusion of information from multiple sources. Fuzzy Integrals (FIs) are widely used aggregation operators, which combine information in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations. However, FIs suffer from the potential drawback of not fusing information according to the intuitively interpretable FM, leading to non-intuitive results. The latter is particularly relevant when a FM has been defined using external information (e.g. experts). In order to address this and provide an alternative to the FI, the Recursive Average (RAV) aggregation operator was recently proposed which enables intuitive data fusion in respect to a given FM. With an alternative fusion operator in place, in this paper, we define the concept of 'A Priori' FMs which are generated based on external information (e.g. classification accuracy) and thus provide an alternative to the traditional approaches of learning or manually specifying FMs. We proceed to develop one specific instance of such an a priori FM to support the decision level fusion step in ensemble classification. We evaluate the resulting approach by contrasting the performance of the ensemble classifiers for different FMs, including the recently introduced Uriz and the Sugeno λmeasure; as well as by employing both the Choquet FI and the RAV as possible fusion operators. Results are presented for 20 datasets from machine learning repositories and contextualised to the wider literature by comparing them to state-of-the-art ensemble classifiers such as Adaboost, Bagging, Random Forest and Majority Voting.

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A first approach towards the usage of classifiers’ performance to create fuzzy measures for ensembles of classifiers: a case study on highly imbalanced datasets

Cited by 6 publications

References 25 publications

Survey on Highly Imbalanced Multi-class Data

Survey on Highly Imbalanced Multi-class Data

An Empirical Study on Supervised and Unsupervised Fuzzy Measure Construction Methods in Highly Imbalanced Classification

Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures

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