Boosting support vector machines for imbalanced data sets
نویسندگان
چکیده
منابع مشابه
Boosting Support Vector Machines
This paper presents a classification algorithm based on Support Vector Machines classifiers combined with Boosting techniques. This classifier presents a better performance in training time, a similar generalization and a similar model complexity but the model representation is more compact.
متن کاملFeature selection for high-dimensional class-imbalanced data sets using Support Vector Machines
Feature selection and classification of imbalanced data sets are two of the most interesting machine learning challenges, attracting a growing attention from both, industry and academia. Feature selection addresses the dimensionality reduction problem by determining a subset of available features to build a good model for classification or prediction, while the class-imbalance problem arises wh...
متن کاملSupport Vector Machines versus Boosting
Support Vector Machines (SVMs) and Adaptive Boosting (AdaBoost) are two successful classification methods. They are essentially the same as they both try to maximize the minimal margin on a training set. In this work, we present an even platform to compare these two learning algorithms in terms of their test error, margin distribution and generalization power. Two basic models of polynomials an...
متن کاملApplying Support Vector Machines to Imbalanced Datasets
Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which negative instances heavily outnumber the positive instances (e.g. in gene profiling and detecting credit card fraud). This paper discusses the factors behind ...
متن کاملAveraging Support Vector Machines for Processing Large Data Sets
The handling of large data sets by support vector machines (SVMs)(Vapnik, 1998) employing a nonlinear kernel suffers from the non-linear scaling of the numerical solution techniques for the underlying optimisation problem. This is in particular valid if the kernel matrix cannot be stored in the main memory anymore and therefore the evaluation of the kernel on given data points needs to be recom...
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: Knowledge and Information Systems
سال: 2009
ISSN: 0219-1377,0219-3116
DOI: 10.1007/s10115-009-0198-y