A dynamic time warping approach for handling class imbalanced medical datasets with missing values: A case study of protein localization site prediction

Keyphrases

• Dynamic Time Warping 100%
• Class Imbalance 100%
• Medical Dataset 100%
• Site Prediction 100%
• Protein Localization 100%
• Minority Class 100%
• Localization Sites 100%
• Missing Value Imputation 50%
• Imputation Methods 50%
• Miss Rate 50%
• Result-oriented 25%
• Measurement Techniques 25%
• Similarity Measure 25%
• Machine Learning Techniques 25%
• Number of Data 25%
• Learning Approaches 25%
• Statistical Learning Techniques 25%
• Missing Attribute Values 25%
• Algorithmic Level 25%
• Non-cancer 25%
• Case Deletion 25%
• Class-imbalanced Datasets 25%
• Class Imbalance Problem 25%
• Skewed Class Distribution 25%
• Mode Imputation 25%
• Deletion Method 25%
• Cost-sensitive Learning 25%
• KNN Imputation 25%
• Cancer Prediction 25%

Computer Science

• Case Study 100%
• Minority Class 100%
• Protein Localization 100%
• Dynamic Time Warping 100%
• Experimental Result 25%
• Machine Learning Technique 25%
• Attribute Value 25%
• Learning Approach 25%
• Similarity Measurement 25%
• Potential Solution 25%
• Class Imbalance Problem 25%
• Class Distribution 25%

Mathematics

• Missing Value 100%
• Imputation Method 28%
• Observed Data 28%