Classification using ensemble learning under weighted misclassification loss
نویسندگان
چکیده
منابع مشابه
Classification of Customer’s Credit Risk Using Ensemble learning (Case study: Sepah Bank)
Banks activities are associated with different kinds of risk such as cresit risk. Considering the limited financial resources of banks to provide facilities, assessment of the ability of repayment of bank customers before granting facilities is one of the most important challenges facing the banking system of the country. Accordingly, in this research, we tried to provide a model for determinin...
متن کاملDynamically Weighted Ensemble Neural Networks for Classification
Combining the outputs of several neural networks into an aggregate output often gives improved accuracy over any individual output. The set of networks is known as an ensemble or committee. This paper presents an ensemble method for classification that has advantages over other techniqes for linear combining. Normally, the output of an ensemble is a weighted sum whose are weights fixed, having ...
متن کاملData Cleaning for Classification Using Misclassification Analysis
In most classification problems, sometimes in order to achieve better results, data cleaning is used as a preprocessing technique. The purpose of data cleaning is to remove noise, inconsistent data and errors in the training data. This should enable the use of a better and representative data set to develop a reliable classification model. In most classification models, unclean data could somet...
متن کاملOptimum Ensemble Classification for Fully Polarimetric SAR Data Using Global-Local Classification Approach
In this paper, a proposed ensemble classification for fully polarimetric synthetic aperture radar (PolSAR) data using a global-local classification approach is presented. In the first step, to perform the global classification, the training feature space is divided into a specified number of clusters. In the next step to carry out the local classification over each of these clusters, which cont...
متن کاملHierarchical Multi-label Classification using Fully Associative Ensemble Learning
Traditional flat classification methods ( e.g. , binary or multi-class classification) neglect the structural information between different classes. In contrast, Hierarchical Multi-label Classification (HMC) considers the structural information embedded in the class hierarchy, and uses it to improve classification performance. In this paper, we propose a local hierarchical ensemble framework fo...
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: Statistics in Medicine
سال: 2019
ISSN: 0277-6715,1097-0258
DOI: 10.1002/sim.8082