An Additive Fahp Based Sentence Score Function for Text Summarization

Güran, Aysun; Uysal, Mitat; Ekinci, Yeliz; Güran, Celal Barkan

doi:10.5755/j01.itc.46.1.13051

Cited by 11 publications

(17 citation statements)

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“…Next, by combining (12), (13), and (14), the ideal solution and the negative ideal solution were obtained. Subsequently, by substituting these solutions and preference weights into (15) and (16), the utility measure and the regret measure were obtained, respectively, and by incorporating these values into (17), the MPCI value (i.e., VIKOR index) was computed. Finally, by using the MPCI value, the experimental samples were ranked.…”

Section: Obtaining Mpcimentioning

confidence: 99%

“…The quality of affective responses has the features of fuzziness. When measuring affective responses, traditional methods such as semantic differential scales and Likert scales employ numerical values that do not exactly represent a perceptual interpretation because human perceptual interpretation of affective responses involves inherent imprecision or vagueness to a certain extent [13][14][15][16]. By contrast, fuzzy sets are a generalization of crisp sets for representing imprecision or vagueness in everyday life, which can serve as a means for modeling the vagueness underlying most natural linguistic terms [17].…”

Section: Introductionmentioning

confidence: 99%

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Application of Fuzzy-Based Hybrid Taguchi Method for Multiobjective Optimization of Product Form Design

Shieh

Yang

et al. 2018

Mathematical Problems in Engineering

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In today’s competitive market, industrial product form design is moving towards being consumer centric. Affective responses relate to customers’ affective needs and are receiving increasing attention. To design a product form that can appeal to consumers, designers should consider multiple affective responses (MARs). This paper proposes a robust design approach that uses a fuzzy-based hybrid Taguchi method to derive the optimal product form design concerning MARs. First, design analysis is used to identify design variables and MARs. According to the results, a Taguchi experiment is designed in which fuzzy sets are used to measure the MARs; then, signal-to-noise (S/N) ratios are calculated. Subsequently, a fuzzy questionnaire with multiple answers is employed to acquire consumers’ preference weights for MARs, following which Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is adopted to transform the multiple S/N ratios into a multiperformance characteristic index (MPCI). On the basis of the MPCI, the effects of design variables are identified through analysis of variance and the response table and the response graph are obtained. Consequently, the optimal form design is achieved. A car profile design was used as an example to demonstrate the proposed approach. The results indicate that this approach can effectively improve consumers’ affective response qualities and can be used as a robust design approach to optimize product form design.

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Section: Obtaining Mpcimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy-Based Hybrid Taguchi Method for Multiobjective Optimization of Product Form Design

Shieh

Yang

et al. 2018

Mathematical Problems in Engineering

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show abstract

“…Naïve Bayes, support vector machine, random forest, and convolutional neural network learning algorithms are used as base classifiers in this study. There are many integration methods that combine decisions of base classifiers to obtain a final decision [8][9][10][11]. For ensemble integration, we used majority voting and stacking methods.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning‐ and Word Embedding‐Based Heterogeneous Classifier Ensembles for Text Classification

Kilimci

Akyokuş

2018

Complexity

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The use of ensemble learning, deep learning, and effective document representation methods is currently some of the most common trends to improve the overall accuracy of a text classification/categorization system. Ensemble learning is an approach to raise the overall accuracy of a classification system by utilizing multiple classifiers. Deep learning-based methods provide better results in many applications when compared with the other conventional machine learning algorithms. Word embeddings enable representation of words learned from a corpus as vectors that provide a mapping of words with similar meaning to have similar representation. In this study, we use different document representations with the benefit of word embeddings and an ensemble of base classifiers for text classification. The ensemble of base classifiers includes traditional machine learning algorithms such as naïve Bayes, support vector machine, and random forest and a deep learning-based conventional network classifier. We analysed the classification accuracy of different document representations by employing an ensemble of classifiers on eight different datasets. Experimental results demonstrate that the usage of heterogeneous ensembles together with deep learning methods and word embeddings enhances the classification performance of texts.

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“…We also calculated the Euclidean distances S + i and S À i using the information in Table 10 and formulas (13) and ( Subsequently, the GC coefficient matrices R + , R À were obtained according to formulas (17) and (20), and the GC degrees R Step 6. Finally, the dimensionless method was applied to normalize S …”

Section: Evaluation Of Traffic Congestion Degreementioning

confidence: 99%

“…MCDM techniques actually assist decision makers to choose the most reasonable program by assessing such problems. As far as we know, many methods exist for evaluating traffic problems: for example, the AHP, [13][14][15][16] the FAHP, [17][18][19] TOPSIS, 20 AHP-TOPSIS, 21 AHP-evidential reasoning, 22,23 GRA, Decision-Making and Evaluation Laboratory (DEMATEL), and the AHP-TOPSIS-GC. 24,25 At present, many scholars have done an in-depth study of traffic problems, and most of researches focus on MCDM techniques.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of traffic congestion degree: An integrated approach

Hao

Feng

Zhang

et al. 2017

International Journal of Distributed Sensor Networks

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Intersection traffic congestion evaluation is essential for effective intelligent transportation system planning, and an objective and precise assessment of traffic congestion is vital to ensure the smooth circulation of traffic. Multiple criteria decision-making is a method for evaluating the degree of traffic congestion. A hybrid multiple criteria decision-making method integrating the fuzzy analytic hierarchy process, techniques for order preference by similarity to an ideal solution, and gray correlation techniques are presented in this work. The proposed method applied fuzzy analytic hierarchy process to determine the weight of the evaluation index; subsequently, gray correlation techniques for order preference by similarity to an ideal solution were integrated to construct the hybrid decision-making method. A case study of traffic congestion at intersections with five evaluation indexes verified the effectiveness of the hybrid method. The evaluation results of the different methods show that the proposed method overcomes the one-sidedness of analytical hierarchy process-techniques for order preference by similarity to an ideal solution and analytical hierarchy process-gray correlation. Thus, the proposed hybrid decision-making model provides a more accurate and reliable method for evaluating the degree of traffic congestion.

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An Additive Fahp Based Sentence Score Function for Text Summarization

Cited by 11 publications

References 0 publications

Application of Fuzzy-Based Hybrid Taguchi Method for Multiobjective Optimization of Product Form Design

Application of Fuzzy-Based Hybrid Taguchi Method for Multiobjective Optimization of Product Form Design

Deep Learning‐ and Word Embedding‐Based Heterogeneous Classifier Ensembles for Text Classification

Evaluation of traffic congestion degree: An integrated approach

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