Multi-Label Classification with Relevance Ordering
نویسندگان
چکیده
منابع مشابه
Distinct Chains for Different Instances: An Effective Strategy for Multi-label Classifier Chains
Multi-label classification (MLC) is a predictive problem in which an object may be associated with multiple labels. One of the most prominent MLC methods is the classifier chains (CC). This method induces q binary classifiers, where q represents the number of labels. Each one is responsible for predicting a specific label. These q classifiers are linked in a chain, such that at classification t...
متن کاملExploiting Associations between Class Labels in Multi-label Classification
Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases ...
متن کاملMLIFT: Enhancing Multi-label Classifier with Ensemble Feature Selection
Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label classifier which utilizes a new strategy to multi-label learning by leveraging label-specific ...
متن کاملAn Improvement of Label Power Set Method Based on Priority Label Transformation
The automatic text categorization and the medical diagnosis were the first domain of applications that requires Multi-label classification. It consists to assign more than one label for each object. Later, its applications were widely increased to cover additional fields like functional genomics, music, biology, scene, video etc... There are two main categories of approaches for Multi-label cla...
متن کاملA New Framework for Distributed Multivariate Feature Selection
Feature selection is considered as an important issue in classification domain. Selecting a good feature through maximum relevance criterion to class label and minimum redundancy among features affect improving the classification accuracy. However, most current feature selection algorithms just work with the centralized methods. In this paper, we suggest a distributed version of the mRMR featu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2011