Evidential instance selection for K-nearest neighbor classification of big data

Gong, Chaoyu; Su, Zhi‐gang; Wang, Peihong; Wang, Qian; You, Yang

doi:10.1016/j.ijar.2021.08.006

Cited by 25 publications

(4 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… K-NN is a classification and regression method first proposed by Cover T and Hart P in 1967. 22 The K-NN method shows good performance in cases where the spatial dimensions of the input variables are small and the actual category boundaries are irregular. SVM is a supervised learning algorithm developed in 1992 by Boser, Guyon, and Vapnik based on statistical learning theory.…”

Section: Methodsmentioning

confidence: 99%

Breast Cancer Prediction Based on Multiple Machine Learning Algorithms

Zhou,

Hu,

Wei

et al. 2024

Technol Cancer Res Treat

View full text Add to dashboard Cite

Introduction The incidence of breast cancer has steadily risen over the years owing to changes in lifestyle and environment. Presently, breast cancer is one of the primary causes of cancer-related deaths among women, making it a crucial global public health concern. Thus, the creation of an automated diagnostic system for breast cancer bears great importance in the medical community. Objectives This study analyses the Wisconsin breast cancer dataset and develops a machine learning algorithm for accurately classifying breast cancer as benign or malignant. Methods Our research is a retrospective study, and the main purpose is to develop a high-precision classification algorithm for benign and malignant breast cancer. To achieve this, we first preprocessed the dataset using standard techniques such as feature scaling and handling missing values. We assessed the normality of the data distribution initially, after which we opted for Spearman correlation analysis to examine the relationship between the feature subset data and the labeled data, considering the normality test results. We subsequently employed the Wilcoxon rank sum test to investigate the dissimilarities in distribution among various breast cancer feature data. We constructed the feature subset based on statistical results and trained 7 machine learning algorithms, specifically the decision tree, stochastic gradient descent algorithm, random forest algorithm, support vector machine algorithm, logistics algorithm, and AdaBoost algorithm. Results The results of the evaluation indicated that the AdaBoost-Logistic algorithm achieved an accuracy of 99.12%, outperforming the other 6 algorithms and previous techniques. Conclusion The constructed AdaBoost-Logistic algorithm exhibits significant precision with the Wisconsin breast cancer dataset, achieving commendable classification performance for both benign and malignant breast cancer cases.

show abstract

Section: Methodsmentioning

confidence: 99%

Breast Cancer Prediction Based on Multiple Machine Learning Algorithms

Zhou,

Hu,

Wei

et al. 2024

Technol Cancer Res Treat

View full text Add to dashboard Cite

show abstract

“…The K-Neighbors Classifier (KNC) [34] method operates by utilizing the k-nearest neighbors algorithm to predict pomegranate growth stages. It assesses the similarity of a given pomegranate sample to its k-nearest neighbors in a training dataset, classifying the growth stage based on the most prevalent stages within that neighborhood.…”

Section: ) K-neighbors Classifiermentioning

confidence: 99%

A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages

Naseer,

Amjad,

Raza

et al. 2024

IEEE Access

View full text Add to dashboard Cite

“…In the context of heart disease, KNN has been used to predict the likelihood of a person having heart disease based on various attributes. For example, one study achieved an accuracy rate of 86.95% using KNN with 12 attributes [37]. The selection of the K parameter in KNN affects the classification results by determining the number of nearest neighbors considered when making predictions.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

Mulyani,

Wijaya,

Trinidya

2024

COMPLETE

View full text Add to dashboard Cite

This study addresses the problem of heart disease detection, a critical concern in public health. The research aims to compare the performance of Convolutional Neural Networks (CNN) with conventional machine learning algorithms in diagnosing heart disease using a dataset comprising 14 features. The primary objective is to determine whether CNNs can provide more accurate and reliable results than traditional techniques. The research employs rigorous preprocessing, normalizing relevant features, and splits the dataset into an 80-20 training-testing split. The model is trained for 300 epochs with a batch size of 64, and performance evaluation is conducted using confusion matrices and classification reports. The results reveal that the CNN model achieved a remarkable accuracy of 100%, demonstrating its potential to outperform conventional machine learning algorithms. These findings emphasize the significance of deep learning techniques in improving heart disease diagnostics, although further research is needed to optimize CNN models and address interpretability concerns for practical implementation in healthcare settings.

show abstract

Evidential instance selection for K-nearest neighbor classification of big data

Cited by 25 publications

References 48 publications

Breast Cancer Prediction Based on Multiple Machine Learning Algorithms

Breast Cancer Prediction Based on Multiple Machine Learning Algorithms

A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

Contact Info

Product

Resources

About