Unified Granular-number-based AHP-VIKOR multi-criteria decision framework

Chatterjee, Kajal; Kar, Samarjit

doi:10.1007/s41066-017-0039-4

Cited by 52 publications

(32 citation statements)

References 54 publications

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“…rule learning and instance-based learning, towards in-depth evaluation of classifiers in terms of their confidence of an individual classification. In addition, it is also worth to investigate the effectiveness of adopting the proposed framework of ensemble learning in the context of multi-attribute decision making (Xu and Wang 2016;Liu and You 2017;Chatterjee and Kar 2017;Lee and Chen 2008;Zulueta-Veliz and Garca-Cabrera 2018), and incorporate fuzzy set theory related techniques (Zadeh 1965;Wang and Chen 2008;Chen et al 2012Chen et al , 2009Chen and Tanuwijaya 2011;Chen and Chen 2001;Chen and Chang 2011;Chen et al 2013) into the proposed framework to achieve fuzzy ensemble learning (Nakai et al 2003).…”

Section: Resultsmentioning

confidence: 99%

Nature-inspired framework of ensemble learning for collaborative classification in granular computing context

Liu

Cocea

2018

Granul. Comput.

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Due to the vast and rapid increase in the size of data, machine learning has become an increasingly popular approach of data classification, which can be done by training a single classifier or a group of classifiers. A single classifier is typically learned by using a standard algorithm, such as C4.5. Due to the fact that each of the standard learning algorithms has its own advantages and disadvantages, ensemble learning, such as Bagging, has been increasingly used to learn a group of classifiers for collaborative classification, thus compensating for the disadvantages of individual classifiers. In particular, a group of base classifiers need to be learned in the training stage, and then some or all of the base classifiers are employed for classifying unseen instances in the testing stage. In this paper, we address two critical points that can impact the classification accuracy, in order to overcome the limitations of the Bagging approach. Firstly, it is important to judge effectively which base classifiers qualify to get employed for classifying test instances. Secondly, the final classification needs to be done by combining the outputs of the base classifiers, i.e. voting, which indicates that the strategy of voting can impact greatly on whether a test instance is classified correctly. In order to address the above points, we propose a nature-inspired approach of ensemble learning to improve the overall accuracy in the setting of granular computing. The proposed approach is validated through experimental studies by using real-life data sets. The results show that the proposed approach overcomes effectively the limitations of the Bagging approach.

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Section: Resultsmentioning

confidence: 99%

Nature-inspired framework of ensemble learning for collaborative classification in granular computing context

Liu

Cocea

2018

Granul. Comput.

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“…In this category of methods, it is tried to find priority relations in non-preferred alternatives so that a set of options is ranked based on them. So far, various decision-making methods have been combined with fuzzy numbers with interval values [56] including the VIKOR method [57,58], the TOPSIS method [59], the MULTIMOORA method [15] and the TODIM method [60]. In this paper, we tried to use Additive Ratio Assessment (ARAS) method combined with fuzzy numbers with interval values which is addressed when we introduce fuzzy numbers with interval values and combined method steps.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Approach for Evaluation of Projects Using an Interval–Valued Fuzzy Additive Ratio Assessment (ARAS) Method: A Case Study of Oil and Gas Well Drilling Projects

et al. 2018

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Abstract:The beginning of the 21st-century resulted in a more developed multi-attribute decisionmaking (MADM) tool and inspired new application areas that have resulted in discoveries in sustainable construction and building life cycle analysis. Construction and civil engineering stand for the central axis of a body consisting of a multidisciplinary (multi-dimensional) world with ties to disciplines constituting the surface, and with the disciplines, as a consequence, tied to each other. When dealing with multi-attribute decision-making problems generally multiple solutions exist, especially when there is a large number of attributes, and the concept of Pareto-optimality is inefficient. The symmetry and structural regularity are essential concepts in many natural and man-made objects and play a crucial role in the design, engineering, and development of the world. The complexity and risks inherent in projects along with different effective indicators for success and failure may contribute to the difficulties in performance evaluation. In such situations, increasing the importance of uncertainty is observed. This paper proposes a novel integrated tool to find a balance between sustainable development, environmental impact and human well-being, i.e., to find symmetry axe with respect to goals, risks, and constraints (attributes) to cope with the complicated problems. The concept of "optimal solution" as the maximum degree of implemented goals (attributes) is very important. The model is built using the most relevant variables cited in the reviewed project literature and integrates two methods: the Step-Wise Weight Assessment Ratio Analysis (SWARA) method and a novel interval-valued fuzzy extension of the Additive Ratio Assessment (ARAS) method. This model was used to solve real case study of oil and gas well drilling projects evaluation. Despite the importance of oil and gas well drilling projects, there is lack of literature that describes and evaluates performance in this field projects. On the other hand, no structured assessment methodology has been presented for these types of projects. Given the limited research on performance evaluation in oil & gas well-drilling projects, the research identifies a set of performance criteria and proposes an evaluation model using fuzzy Delphi method. An illustrative example shows that the proposed method is a useful and alternative decision-making method.

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“…On the other hand, classes may also have horizontal relationships between each other when these classes are at the same level of granularity, such as mutual exclusion, correlation and mutual independence ). In practice, granular computing concepts and techniques have been used broadly in popular areas, such as artificial intelligence (Wilke and Portmann 2016;Pedrycz and Chen 2011;Skowron et al 2016), computational intelligence (Dubois and Prade 2016;Yao 2005b;Kreinovich 2016;Livi and Sadeghian 2016), machine learning (Min and Xu 2016;Peters and Weber 2016;Liu and Cocea 2017c;Antonelli et al 2016), decision making (Xu and Wang 2016;Liu and You 2017;Chatterjee and Kar 2017) and data clustering (Chen et al 2009;Horng et al 2005).…”

Section: Granular Computingmentioning

confidence: 99%

Multi-task learning for intelligent data processing in granular computing context

2017

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Classification is a popular task in many application areas, such as decision making, rating, sentiment analysis and pattern recognition. In the recent years, due to the vast and rapid increase in the size of data, classification has been mainly undertaken in the way of supervised machine learning. In this context, a classification task involves data labelling, feature extraction, feature selection and learning of classifiers. In traditional machine learning, data is usually single-labelled by experts, i.e., each instance is only assigned one class label, since experts assume that different classes are mutually exclusive and each instance is clear-cut. However, the above assumption does not always hold in real applications. For example, in the context of emotion detection, there could be more than one emotion identified from the same person. On the other hand, feature selection has typically been done by evaluating feature subsets in terms of their relevance to all the classes. However, it is possible that a feature is only relevant to one class, but is irrelevant to all the other classes. Based on the above argumentation on data labelling and feature selection, we propose in this paper a framework of multi-task learning. In particular, we consider traditional machine learning to be single task learning, and argue the necessity to turn it into multi-task learning to allow an instance to belong to more than one class (i.e., multi-task classification) and to achieve class specific feature selection (i.e., multi-task feature selection). Moreover, we report two experimental studies in terms of fuzzy multi-task classification and rule learning based multi-task feature selection. The results show empirically that it is necessary to undertake multi-task learning for both classification and feature selection.

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Unified Granular-number-based AHP-VIKOR multi-criteria decision framework

Cited by 52 publications

References 54 publications

Nature-inspired framework of ensemble learning for collaborative classification in granular computing context

Nature-inspired framework of ensemble learning for collaborative classification in granular computing context

A Novel Approach for Evaluation of Projects Using an Interval–Valued Fuzzy Additive Ratio Assessment (ARAS) Method: A Case Study of Oil and Gas Well Drilling Projects

Multi-task learning for intelligent data processing in granular computing context

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