Improved Focused Sampling for Class Imbalance Problem
نویسندگان
چکیده
منابع مشابه
Semi Supervised Under-sampling: a Solution to the Class Imbalance Problem for Classification and Feature Selection
Most medical datasets are not balanced in their class labels. Furthermore, in some cases it has been noticed that the given class labels do not accurately represent characteristics of the data record. Most existing classification methods tend not to perform well on minority class examples when the dataset is extremely imbalanced. This is because they aim to optimize the overall accuracy without...
متن کاملAdaptive Sampling with Optimal Cost for Class-Imbalance Learning
Learning from imbalanced data sets is one of the challenging problems in machine learning, which means the number of negative examples is far more than that of positive examples. The main problems of existing methods are: (1) The degree of re-sampling, a key factor greatly affecting performance, needs to be pre-fixed, which is difficult to make the optimal choice; (2) Many useful negative sampl...
متن کاملHybrid Sampling with Bagging for Class Imbalance Learning
For class imbalance problem, the integration of sampling and ensemble methods has shown great success among various methods. Nevertheless, as the representatives of sampling methods, undersampling and oversampling cannot outperform each other. That is, undersampling fits some data sets while oversampling fits some other. Besides, the sampling rate also significantly influences the performance o...
متن کاملClass Imbalance Problem in Data Mining Review
In last few years there are major changes and evolution has been done on classification of data. As the application area of technology is increases the size of data also increases. Classification of data becomes difficult because of unbounded size and imbalance nature of data. Class imbalance problem become greatest issue in data mining. Imbalance problem occur where one of the two classes havi...
متن کاملThe Class Imbalance Problem: Signiicance and Strategies
Although the majority of concept-learning systems previously designed usually assume that their training sets are well-balanced, this assumption is not necessarily correct. Indeed, there exist many domains for which one class is represented by a large number of examples while the other is represented by only a few. The purpose of this paper is 1) to demonstrate experimentally that, at least in ...
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: The KIPS Transactions:PartB
سال: 2007
ISSN: 1598-284X
DOI: 10.3745/kipstb.2007.14-b.4.287