Intelligent synthetic composite indicators with application

Shami, Ahmad Al; Lotfi, Ahmad; Coleman, Simeon

doi:10.1007/s00500-013-1098-3

Cited by 6 publications

(7 citation statements)

References 26 publications

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“…Similarly, FCM-based imputation also has some disadvantages. For example, OCS [30][31] used initial values and available feature values to calculate FCM cluster prototypes, and the missing values were estimated based on these biased cluster prototypes. Hence, the computation of the FCM cluster prototype and the imputation of the missing value would influence each other.…”

Section: Discussion and Findingsmentioning

confidence: 99%

“…Hathaway and Bezdek [30] proposed four FCM imputation techniques, and they pointed out that whole data strategy and partial distance strategy are faster to end, but the optimal completion strategy (OCS) and nearest prototype strategy (NPS) were outperformed over the first two methods based on accuracy and misclassification errors. In addition, Al Shami et al [31] applied FCM-based OCS and NPS to compare four statistical imputation methods in their work for accurately substitute missing scores when producing the intelligent synthetic composite indicators. Therefore, we briefly introduced FCM-based OCS and NPS methods as follows.…”

Section: Computational Intelligence Imputationmentioning

confidence: 99%

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A novel clustering-based purity and distance imputation for handling medical data with missing values

Cheng

Huang

2021

Soft Comput

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Nowadays, people pay increasing attention to health, and the integrity of medical records has been put into focus. Recently, medical data imputation has become a very active field because medical data usually have missing values. Many imputation methods have been proposed, but many model-based imputation methods such as expectation-maximization and regression-based imputation based on the variables data have a multivariate normal distribution, which assumption can lead to biased results. Sometimes this becomes a bottleneck, such as computationally more complex than model-free methods. Furthermore, directly remove instances with missing values, this approach has several problems, and it is possible to lose the important data, produce ineffective research samples, and cause research deviations, and so on.Therefore, this study proposes a novel clustering-based purity and distance imputation method to improve the handling of missing values. In the experiment, we collected eight different medical datasets to compare the proposed imputation methods with the listed imputation methods with regard to the results of different situations. In imputation measures, the area under the curve (AUC) is used to evaluate the performance of the imbalanced class datasets in MAR and MCAR experiments, and accuracy is applied to measure its performance of the balanced class in MNAR experiment. Finally, the root-mean-square error (RMSE) is also used to compare the proposed and the listing imputation methods. In addition, this study utilized the elbow method and the average silhouette method to find the optimal number of clusters for all datasets. Results showed that the proposed imputation method could improve imputation performance in the accuracy, AUC, and RMSE of different missing degrees and missing types.

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Section: Discussion and Findingsmentioning

confidence: 99%

Section: Computational Intelligence Imputationmentioning

confidence: 99%

A novel clustering-based purity and distance imputation for handling medical data with missing values

Cheng

Huang

2021

Soft Comput

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“…Finally, the provided answers were analyzed using the Fuzzy Proximity Knowledge Mining technique, as explained by Al Shami et al (2013), and the findings were assembled in an infographic presentation. This was achieved by searching and finding the most frequently matched key lexical phrases for a certain question.…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, the results of this query are usually presented in Tree or Word Map tabs which display the most frequently used terms by the respondents as a series of rectangles in which frequently occurring words are in larger rectangles (Al Shami et al, 2013).…”

Section: Methodsmentioning

confidence: 99%

Deep learning analysis of social media content used by Bahraini women: WhatsApp in focus

Elareshi

Ziani

Shami

2020

Convergence: The International Journal of Research into New Med

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This article examines perceptions of social media use (e.g. the WhatsApp application), in particular looking at how Bahraini women use such technology and how mobile communication is used by such a segment of the population. Mobile devices are very accessible to Bahraini women and this needs even further study to learn ways of using applications for information and other things. This analysis is based on an online survey, conducted among 1137 Bahraini women, using a nonrepresentative sample (snowball). Such data were analyzed using a deep learning approach which utilizes, in particular, the Fuzzy Proximity Knowledge Mining technique to examine the provided answers. The study found that WhatsApp has enabled Bahraini women to communicate and share information with others. They spent 2–3 h daily sending and enjoying comics and entertainment clips and important and rare news stories. Social interaction, communication, and escapism featured strongly as the most popular reasons for using WhatsApp. Overall, WhatsApp served as a platform used to participate in social and communication activities.

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“…Otherwise process will repeat again from step 2. After a number of iterations, cluster centres will satisfy the minimisation of the cost function J to a local minimum [19].…”

Section: A Compared Techniques: K-means Vs Fuzzy C-meansmentioning

confidence: 99%