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Keyword: Boosting
An Ensemble Learning Approach for Student Performance Analysis of a Higher Educational Institute using a SHAP-Based Feature Selection and Optuna Optimization
Forecasting and assessing student performance are crucial for allowing educators to pinpoint deficiencies and promote grade improvement. A thorough comprehension of feature contributions is crucial for improving model interpretability and facilitating informed decision-making in academic institutions. Explainable artificial intelligence encompasses methodologies and strategies designed to deliver transparent and accessible rationales for the decisions rendered by…
Read More Utilizing 3D models for the Prediction of Work Man-Hour in Complex Industrial Products using Machine Learning
The integration of machine learning techniques in industrial production has the potential to revolutionize traditional manufacturing processes. In this study, we examine the efficacy of gradient-boosting machine learning models, specifically focusing on feature engineering techniques, applied to a novel dataset with 3D product models pertaining to work moan-hours in metal sheet stamping projects, framed as…
Read More Tree-Based Ensemble Models, Algorithms and Performance Measures for Classification
An ensemble method is a Machine Learning (ML) algorithm that aggregates the predictions of multiple estimators or models. The purpose of an ensemble module is to provide better predictive performance than any single contributing model. This can be achieved by producing a predictive model with reduced variance using bagging, and bias using boosting. The Tree-Based…
Read More Ensemble Extreme Learning Algorithms for Alzheimer’s Disease Detection
Alzheimer’s disease has proven to be the major cause of dementia in adults, making its early detection an important research goal. We have used Ensemble ELMs (Extreme Learning Models) on the OASIS (Open Access Series of Imaging Studies) data set for Alzheimer’s detection. We have explored various single layered light-weight ELM networks. This is an…
Read More Interpretation of Machine Learning Models for Medical Diagnosis
Machine learning has been dramatically advanced over several decades, from theory context to a general business and technology implementation. Especially in healthcare research, it is obvious to perceive the scrutinizing implementation of machine learning to warranty the rewarded benefits in early disease detection and service recommendation. Many practitioners and researchers have eventually recognized no absolute…
Read More Predictive Modelling of Student Dropout Using Ensemble Classifier Method in Higher Education
Currently, one of the challenges of educational institutions is drop-out student issues. Several factors have been found and determined potentially capable to stimulate dropouts. Many researchers have been applied data mining methods to analyze, predict dropout students and also optimize finding dropout variables in advance. The main objective of this study is to find the…
Read More Aggrandized Random Forest to Detect the Credit Card Frauds
From the collection of supervised machine learning technique, an ensemble procedure is used in Random Forest. In the arena of Data mining, there is an excellent claim for machine learning techniques. Random Forest has tremendous latent of becoming a widespread technique for forthcoming classifiers as its performance has been found analogous with ensemble techniques bagging…
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