Addressing the Class Imbalance Problem in Medical Datasets
نویسندگان
چکیده
منابع مشابه
Addressing the Class Imbalance Problem in Medical Datasets
A well balanced dataset is very important for creating a good prediction model. Medical datasets are often not balanced in their class labels. Most existing classification methods tend to perform poorly on minority class examples when the dataset is extremely imbalanced. This is because they aim to optimize the overall accuracy without considering the relative distribution of each class. In thi...
متن کاملNew Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification
In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified cla...
متن کاملThe Class Imbalance Problem in Author Identification
Author identification can be seen as a single-label multi-class text categorization problem. Very often, there are extremely few training texts at least for some of the candidate authors or there is a significant variation in the text-length among the available training texts of the candidate authors. Moreover, in this task usually there is no similarity between the distribution of training and...
متن کاملActive Learning for Word Sense Disambiguation with Methods for Addressing the Class Imbalance Problem
In this paper, we analyze the effect of resampling techniques, including undersampling and over-sampling used in active learning for word sense disambiguation (WSD). Experimental results show that under-sampling causes negative effects on active learning, but over-sampling is a relatively good choice. To alleviate the withinclass imbalance problem of over-sampling, we propose a bootstrap-based ...
متن کاملLearning Greek Verb Complements: Addressing the Class Imbalance
Imbalanced training sets, where one class is heavily underrepresented compared to the others, have a bad effect on the classification of rare class instances. We apply One-sided Sampling for the first time to a lexical acquisition task (learning verb complements from Modern Greek corpora) to remove redundant and misleading training examples of verb nondependents and thereby balance our training...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Machine Learning and Computing
سال: 2013
ISSN: 2010-3700
DOI: 10.7763/ijmlc.2013.v3.307