KBA: kernel boundary alignment considering imbalanced data distribution
نویسندگان
چکیده
منابع مشابه
Class-Boundary Alignment for Imbalanced Dataset Learning
In this paper, we propose the class-boundaryalignment algorithm to augment SVMs to deal with imbalanced training-data problems posed by many emerging applications (e.g., image retrieval, video surveillance, and gene profiling). Through a simple example, we first show that SVMs can be ineffective in determining the class boundary when the training instances of the target class are heavily outnum...
متن کاملAsymmetric Kernel Scaling for Imbalanced Data Classification
Many critical application domains present issues related to imbalanced learning classification from imbalanced data. Using conventional techniques produces biased results, as the over-represented class dominates the learning process and tend to naturally attract predictions. As a consequence, the false negative rate may result unacceptable and the chosen classifier unusable. We propose a classi...
متن کاملmodeling loss data by phase-type distribution
بیمه گران همیشه بابت خسارات بیمه نامه های تحت پوشش خود نگران بوده و روش هایی را جستجو می کنند که بتوانند داده های خسارات گذشته را با هدف اتخاذ یک تصمیم بهینه مدل بندی نمایند. در این پژوهش توزیع های فیزتایپ در مدل بندی داده های خسارات معرفی شده که شامل استنباط آماری مربوطه و استفاده از الگوریتم em در برآورد پارامترهای توزیع است. در پایان امکان استفاده از این توزیع در مدل بندی داده های گروه بندی ...
On Kernel-Target Alignment
We introduce the notion of kernel-alignment, a measure of similarity between two kernel functions or between a kernel and a target function. This quantity captures the degree of agreement between a kernel and a given learning task, and has very natural interpretations in machine learning, leading also to simple algorithms for model selection and learning. We analyse its theoretical properties, ...
متن کاملOptimization of Kernel Alignment by Data Translation in Feature Space
Kernel-target alignment is commonly used to predict the behavior of reproducing kernels in a classification context, without training any kernel machine. In this paper, we show that a poor position of training data in feature space can drastically reduce the value of alignment. This implies that, in a kernel selection setting, the best kernel of a given collection may be characterized by a low ...
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: IEEE Transactions on Knowledge and Data Engineering
سال: 2005
ISSN: 1041-4347
DOI: 10.1109/tkde.2005.95