Semisupervised Feature Selection with Universum
نویسندگان
چکیده
منابع مشابه
Feature Selection Algorithm for Supervised and Semisupervised Clustering
−In clustering process, semi-supervised learning is a tutorial of contrivance learning methods that make usage of both labeled and unlabeled data for training characteristically a trifling quantity of labeled data with a great quantity of unlabeled data. Semi-supervised learning cascades in the middle of unsupervised learning (without any labeled training data) and supervised learning (with com...
متن کاملSemisupervised learning using feature selection based on maximum density subgraphs
We present a new graph based semi-supervised learning algorithm, using multiway cut on a neighborhood graph to achieve an optimum classification. We also present a graph based feature selection algorithm utilizing the global structure of the graph derived from both labeled and unlabeled examples. With respect to the experiments we conducted, both of our approaches are proved to have a promising...
متن کاملSupervised and Semisupervised Clustering Based on Feature Selection Algorithm Process
In clustering process, semi-supervised learning is a tutorial of contrivance learning methods that make usage of both labeled and unlabeled data for training characteristically a trifling quantity of labeled data with a great quantity of unlabeled data. Semi-supervised learning cascades in the middle of unsupervised learning (without any labeled training data) and supervised learning (with comp...
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 کاملUBoost: Boosting with the Universum
It has been shown that the Universum data, which do not belong to either class of the classification problem of interest, may contain useful prior domain knowledge for training a classifier [1], [2]. In this work, we design a novel boosting algorithm that takes advantage of the available Universum data, hence the name UBoost. UBoost is a boosting implementation of Vapnik's alternative capacity ...
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: Mathematical Problems in Engineering
سال: 2016
ISSN: 1024-123X,1563-5147
DOI: 10.1155/2016/5874161