Improving the Accuracy in Classification Using the Bayesian Relevance Feedback (BRF) Model

Mohd, Fatihah; Jalil, Masita Abdul; Noor, Noor Maizura Mohamad; Bakar, Zainab Abu

doi:10.1109/infrkm.2018.8464783

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…It combines rules from the KB with internal data in databases to generate new knowledge. This new knowledge will be used by users to help make decisions [12]. The user interface controls the acceptance of input from and output displays to decision makers.…”

Section: Decision Support Systemmentioning

confidence: 99%

An Architecture of Decision Support System for Visual-Auditory-Kinesthetic (VAK) Learning Styles Detection Through Behavioral Modelling

Mohd¹,

Yahya²,

Ismail³

et al. 2019

IJIES

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Learning style (LS) is a description of the attitudes and behaviors which determine an individual’s preferred way of learning. Since each student has different LS, it is important for the teacher to recognize the differences in LS. Thus, an appropriate technique to detect students' LS, improve the motivation and academic achievement are required. The common approach using questionnaires to identify LS is less accurate due to complete the questionnaire is a tedious task for students and tend to choose answers randomly without understanding the questions. Emotions such as anger, sadness, and happiness resulting the different questionnaire answers. Due to the approach constrains, this study has focused on automated approaches that identify student LS from student behavior in the learning process. Implementation of decision support system (DSS) as automated application systems is needed to help teachers make decisions in determining students' LS. Thus, the objective of this study is to propose the architecture of LS detection automatically using decision support system. The development of the architecture is applying the behavioral modelling, that are contained student’s behavior parameters for visual-auditory-kinesthetic (VAK) model. Evaluation of the architecture is tested with the precision DSS engine. The accuracy of the rule technique achieves significant 80% accuracy. This study aims to help teachers to identify the ability of the student through the learning style (LS) in order to create effectiveness of learning and improving student’s achievement indirectly. Keywords— decision support system, reasoning engines, learning style detection, user behavior, visual-auditory-kinesthetic (VAK) model

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Section: Decision Support Systemmentioning

confidence: 99%

An Architecture of Decision Support System for Visual-Auditory-Kinesthetic (VAK) Learning Styles Detection Through Behavioral Modelling

Mohd¹,

Yahya²,

Ismail³

et al. 2019

IJIES

Self Cite

View full text Add to dashboard Cite

show abstract

“…22,23 • K-nearest Neighbours: KNN is included due to its capability to handle complex, non-linear interactions between variables without a priori assumptions about data distribution. ward replication approach to augment minority classes, and SMOTE for generating synthetic yet plausible samples through interpolation.…”

mentioning

confidence: 99%

Application of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery: Acomprehensive study

Benítez-Andrades,

Prada-García,

García-Fernández

et al. 2024

DIGITAL HEALTH

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Objectives Metabolic bariatric surgery is a critical intervention for patients living with obesity and related health issues. Accurate classification and prediction of patient outcomes are vital for optimizing treatment strategies. This study presents a novel machine learning approach to classify patients in the context of metabolic bariatric surgery, providing insights into the efficacy of different models and variable types. Methods Various machine learning models, including Gaussian Naive Bayes, Complement Naive Bayes, K-nearest neighbour, Decision Tree, K-nearest neighbour with RandomOverSampler, and K-nearest neighbour with SMOTE, were applied to a dataset of 73 patients. The dataset, comprising psychometric, socioeconomic, and analytical variables, was analyzed to determine the most efficient predictive model. The study also explored the impact of different variable groupings and oversampling techniques. Results Experimental results indicate average accuracy values as high as 66.7% for the best model. Enhanced versions of K-nearest neighbour and Decision Tree, along with variations of K-nearest neighbour such as RandomOverSampler and SMOTE, yielded the best results. Conclusions The study unveils a promising avenue for classifying patients in the realm of metabolic bariatric surgery. The results underscore the importance of selecting appropriate variables and employing diverse approaches to achieve optimal performance. The developed system holds potential as a tool to assist healthcare professionals in decision-making, thereby enhancing metabolic bariatric surgery outcomes. These findings lay the groundwork for future collaboration between hospitals and healthcare entities to improve patient care through the utilization of machine learning algorithms. Moreover, the findings suggest room for improvement, potentially achievable with a larger dataset and careful parameter tuning.

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Soft Classification Techniques for Breast Cancer Detection and Classification

Ivaturi

Singh

Gunanvitha

et al. 2020

2020 International Conference on Intelligent Engineering and Management (ICIEM)

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Improving the Accuracy in Classification Using the Bayesian Relevance Feedback (BRF) Model

Cited by 3 publications

References 18 publications

An Architecture of Decision Support System for Visual-Auditory-Kinesthetic (VAK) Learning Styles Detection Through Behavioral Modelling

An Architecture of Decision Support System for Visual-Auditory-Kinesthetic (VAK) Learning Styles Detection Through Behavioral Modelling

Application of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery: Acomprehensive study

Soft Classification Techniques for Breast Cancer Detection and Classification

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