A Choquet integral-based approach to multiattribute decision-making with correlated periods

Véliz, Yeleny Zulueta; García-Cabrera, Lina

doi:10.1007/s41066-018-0095-4

Cited by 15 publications

(6 citation statements)

References 64 publications

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“…In future, it is worth to investigate in more depth the diversity creation through deep feature extraction and selection in the setting of multi-granularity learning Cocea, 2017a, 2018). Moreover, 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%

Shape recognition through multi-level fusion of features and classifiers

et al. 2019

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Shape recognition is a fundamental problem and a special type of image classification, where each shape is considered as a class. Current approaches to shape recognition mainly focus on designing low-level shape descriptors, and classify them using some machine learning approaches. In order to achieve effective learning of shape features, it is essential to ensure that a comprehensive set of high quality features can be extracted from the original shape data. Thus we have been motivated to develop methods of fusion of features and classifiers for advancing the classification performance. In this paper, we propose a multi-level framework for fusion of features and classifiers in the setting of granular computing. The proposed framework involves creation of diversity among classifiers, through adopting feature selection and fusion to create diverse feature sets and to train diverse classifiers using different learn

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Section: Resultsmentioning

confidence: 99%

Shape recognition through multi-level fusion of features and classifiers

et al. 2019

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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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“…Furthermore, we will develop new ways of diversity creation of rule-based classifiers that are trained based on Boolean logic in the case of the absence of continuous attributes. Moreover, we will look to apply the proposed rule learning approach in the context of multi-criteria decision making [46,49,50]. In addition, it is worth to conduct in-depth investigation of granular computing techniques [1,3,27,30,48] to achieve deep learning of fuzzy rules in a multi-granularity manner [24], according to the inspiration of deep neural networks [47].…”

Section: Discussionmentioning

confidence: 99%

Multi-stage mixed rule learning approach for advancing performance of rule-based classification

Liu

Chen

2019

Information Sciences

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Rule learning is a special type of machine learning approaches, and its key advantage is the generation of interpretable models, which provides a transparent process of showing how an input is mapped to an output. Traditional rule learning algorithms are typically based on Boolean logic for inducing rule antecedents, which are very effective for training models on data sets that involve discrete attributes only. When continuous attributes are present in a data set, traditional rule learning approaches need to employ crisp intervals. However, in reality, problems usually show shades of grey, which motivated the development of fuzzy rule learning approaches by employing fuzzy intervals for handling continuous attributes. While a data set contains a large portion of discrete attributes or even no continuous attributes, fuzzy approaches cannot be used to learn rules effectively, leading to a drop in the performance. In this paper, a multi-stage approach of mixed rule learning is proposed, which involves strategic combination of both traditional and fuzzy approaches to handle effectively various types of attributes. We compare our proposed approach with existing algorithms of rule learning. Our experimental results show that our proposed approach leads to significant advances in the performance compared with the existing algorithms.

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A Choquet integral-based approach to multiattribute decision-making with correlated periods

Cited by 15 publications

References 64 publications

Shape recognition through multi-level fusion of features and classifiers

Shape recognition through multi-level fusion of features and classifiers

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

Multi-stage mixed rule learning approach for advancing performance of rule-based classification

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