A framework to Deal with Missing Data in Data Sets

Shalabi, Luai Al; Najjar, Mohannad; Kayed, Ahmad

doi:10.3844/jcssp.2006.740.745

Cited by 14 publications

(6 citation statements)

References 19 publications

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“…Removing such noise will improve the accuracy of the dataset. Al-Shalabi et al (2006) highlighted some reasons for missing data including the followings: the value is not relevant to a particular case, not recorded, or ignored because of privacy concerns. One of the solutions for missing data is to delete all records that have missing data (Dempster et al, 1977).…”

Section: Data Preprocessingmentioning

confidence: 99%

A Data Mining Model for Students’ Choice of College Major Based on Rough Set Theory

Al-Shalabi¹

2019

Journal of Computer Science

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Literature is focusing on identifying factors that influence students' initial choice of major and few have studied students' involvements after registration in a selected major and this study is one of the few. This study aims to determine the important factors that influence high school students' choice of major based on data mining techniques. A questionnaire was designed to collect data from students in different universities in Kuwait and in different faculties such as science, literature, medicine and engineering. Rough set theory for feature selection was used to highlight and explain the significant factors related to students' skills and preferences awareness as well as their experience reflection that are responsible for the development of their satisfaction with the choice of their university majors. The findings of the study revealed that the calculated reducts have a significant influence on the students' choice of the university and collage major. This research contributes to literature by identifying the relationship between the conditional factors of the reduct (also known as the independent variables) and the classification attribute (also known as the dependent variable). The results of the study give valuable information to the high school students so they know the best majors which suite their skills, preference and experiences. This research also help students not to continually change their major because of the wrong choice of major they made which accordingly lead them to dissatisfaction of their major.

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Section: Data Preprocessingmentioning

confidence: 99%

A Data Mining Model for Students’ Choice of College Major Based on Rough Set Theory

Al-Shalabi¹

2019

Journal of Computer Science

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“…The proportion of missing data in some samples or features is relatively large. They are meaningless for research because they contained little useful information [ 20 ]. So, this study chooses to delete them directly.…”

Section: Methodsmentioning

confidence: 99%

A Machine-Learning Method of Predicting Vital Capacity Plateau Value for Ventilatory Pump Failure Based on Data Mining

Chang

Wang

et al. 2021

Healthcare

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Ventilatory pump failure is a common cause of death for patients with neuromuscular diseases. The vital capacity plateau value (VCPLAT) is an important indicator to judge the status of ventilatory pump failure for patients with congenital myopathy, Duchenne muscular dystrophy and spinal muscular atrophy. Due to the complex relationship between VCPLAT and the patient’s own condition, it is difficult to predict the VCPLAT for pediatric disease from a medical perspective. We established a VCPLAT prediction model based on data mining and machine learning. We first performed the correlation analysis and recursive feature elimination with cross-validation (RFECV) to provide high-quality feature combinations. Based on this, the Light Gradient Boosting Machine (LightGBM) algorithm was to establish a prediction model with powerful performance. Finally, we verified the validity and superiority of the proposed method via comparison with other prediction models in similar works. After 10-fold cross-validation, the proposed prediction method had the best performance and its explained variance score (EVS), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), median absolute error (MedAE) and R2 were 0.949, 0.028, 0.002, 0.045, 0.015 and 0.948, respectively. It also performed well on test datasets. Therefore, it can accurately and effectively predict the VCPLAT, thereby determining the severity of the condition to provide auxiliary decision-making for doctors in clinical diagnosis and treatment.

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“…Zou [121] presents a framework based on the number of records, number of attributes, number of symbolic attributes, database entropy, number of classes, and missing data rate. Shalabi et al [98] propose a framework that implements four techniques for treating missing data, and the choice of the technique to be used is based on the rough sets theory.…”

Section: Procedures Based On Direct Manipulation Of Missing Datamentioning

confidence: 99%

A brief review of the main approaches for treatment of missing data

Silva

Zárate

2014

IDA

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Missing data is a significant problem found in data mining projects and data analysis. Despite being a common problem, the missing data is dealt in a simplistic way and may lead to inconsistent knowledge discovery. Through literature review, it was possible to observe that the missing data mechanisms are not always considered when methods of treatment or imputation are chosen. This work presents a review about the main treatment methods of missing data that can be considered in a process of knowledge discovery in database. We emphasize that attention should be given to the identification of the absence mechanism, for the choice the most appropriate treatment method.

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A framework to Deal with Missing Data in Data Sets

Cited by 14 publications

References 19 publications

A Data Mining Model for Students’ Choice of College Major Based on Rough Set Theory

A Data Mining Model for Students’ Choice of College Major Based on Rough Set Theory

A Machine-Learning Method of Predicting Vital Capacity Plateau Value for Ventilatory Pump Failure Based on Data Mining

A brief review of the main approaches for treatment of missing data

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