Fuzzy information granulation towards interpretable sentiment analysis

Liu, Han; Cocea, Mihaela

doi:10.1007/s41066-017-0043-8

Cited by 46 publications

(13 citation statements)

References 45 publications

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“…Fuzzy logic is also applicable in modeling and control [21], machine learning and data mining [22]. The fuzzy information granulation is applied in the sentiment analysis which is also called opinion mining in [23] considering the aspect of interpretability. The objective is to recognize the emotions or the attitude of the people by using the concept of natural language processing.…”

Section: Related Workmentioning

confidence: 99%

Interpretability and Accuracy Analysis of Fuzzy Rule Based System Designed for Abalone

Chandra*,

Agarwal,

Shukla

2020

IJRTE

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 Abstract: Fuzzy Rule Based Systems are playing vital role in the implementation of human decision making. The development of interpretable Fuzzy Rule Based Systems with improved accuracy is a crucial research aspect in fuzzy based systems. Mamdani type fuzzy rule based systems are used to implement the proposed model. In this manuscript a FRBS is implemented with Guaje Open-Access Java based software. The interpretability and accuracy assessments are recorded on the different experiments with various rule generation methods, like Fuzzy decision tree and Wang Mendel method. The results are found satisfactory and a trade-off is handled between interpretability and accuracy. The major concern of the experimentation is number and type of fuzzy partitions. K-means and Hierarchical Fuzzy Partitions are used in the experiments with three and five number of fuzzy partitions.

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Section: Related Workmentioning

confidence: 99%

Interpretability and Accuracy Analysis of Fuzzy Rule Based System Designed for Abalone

Chandra*,

Agarwal,

Shukla

2020

IJRTE

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“…All elements within the boundary region can have a conditional place, because they only partially satisfy the conditions for getting into an unbounded region of the set [21]. While the conditions would be completely fulfilled, these elements would be unconditional members of a particular set.…”

Section: Confidence Factorsmentioning

confidence: 99%

Fuzzy and Reflection in the Construction of a Medical Expert System

Кобринский¹

2020

JSEA

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The fuzzy of symptoms (including visual images), representations and assessments in medicine correspond to the peculiarities of the picture of the world of the patient and the physician taking into account the influence of reflection. The continuum of intermediate characteristics of the signs creates serious difficulty for their assessment by physicians. Experts' confidence factors not only for linguistic features, but also for visual images can help increase the hypothesis quality in intelligent medical diagnostic systems.

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“…The former operation is aimed at decomposition of a whole into different parts, whereas the latter operation is aimed at integrating several parts into a whole. In computer science, the concepts of granulation and organization have been popularly used to achieve the top-down and bottom-up approaches, respectively (Liu and Cocea 2017a;). In the context of ensemble learning, the Bagging approach involves random sampling of training data with replacement, which essentially follows the principle of information granulation.…”

Section: Justificationmentioning

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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Fuzzy information granulation towards interpretable sentiment analysis

Cited by 46 publications

References 45 publications

Interpretability and Accuracy Analysis of Fuzzy Rule Based System Designed for Abalone

Interpretability and Accuracy Analysis of Fuzzy Rule Based System Designed for Abalone

Fuzzy and Reflection in the Construction of a Medical Expert System

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

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