Feature Selection Inspired Classifier Ensemble Reduction
نویسندگان
چکیده
منابع مشابه
MLIFT: 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 کاملPrediction of Lysine Ubiquitylation with Ensemble Classifier and Feature Selection
Ubiquitylation is an important process of post-translational modification. Correct identification of protein lysine ubiquitylation sites is of fundamental importance to understand the molecular mechanism of lysine ubiquitylation in biological systems. This paper develops a novel computational method to effectively identify the lysine ubiquitylation sites based on the ensemble approach. In the p...
متن کاملEnhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble
In this paper ensemble learning based feature selection and classifier ensemble model is proposed to improve classification accuracy. The hypothesis is that good feature sets contain features that are highly correlated with the class from ensemble feature selection to SVM ensembles which can be achieved on the performance of classification accuracy. The proposed approach consists of two phases:...
متن کاملFeature Selection Ensemble
Many strategies have been exploited for the task of feature selection, in an effort to identify more compact and better quality feature subsets. Such techniques typically involve the use of an individual feature significance evaluation, or a measurement of feature subset consistency, that work together with a search algorithm in order to determine a quality subset. Feature selection ensemble ai...
متن کاملFeature Reduction Using Ensemble Approach
The performance of many content analysis methods heavily dependent on the features they are applied. A fundamental problem that makes the content analysis difficult is the curse of dimensionality. In this study, we propose a novel feature reduction method which adopts ensemble approach to measure the divergence between the training set and test set and use the divergence to supervise the featur...
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 Cybernetics
سال: 2014
ISSN: 2168-2267,2168-2275
DOI: 10.1109/tcyb.2013.2281820