Twin Boosting: improved feature selection and prediction
نویسندگان
چکیده
منابع مشابه
Twin Boosting: improved feature selection and prediction
We propose Twin Boosting which has much better feature selection behavior than boosting, particularly with respect to reducing the number of false positives (falsely selected features). In addition, for cases with a few important effective and many noise features, Twin Boosting also substantially improves the predictive accuracy of boosting. Twin Boosting is as general and generic as boosting. ...
متن کاملAn improved boosting based on feature selection for corporate bankruptcy prediction
With the recent financial crisis and European debt crisis, corporate bankruptcy prediction has become an increasingly important issue for financial institutions. Many statistical and intelligent methods have been proposed, however, there is no overall best method has been used in predicting corporate bankruptcy. Recent studies suggest ensemble learning methods may have potential applicability i...
متن کاملRobust twin boosting for feature selection from high-dimensional omics data with label noise
Omics data such as microarray transcriptomic and mass spectrometry proteomic data are typically characterized by high dimensionality and relatively small sample sizes. In order to discover biomarkers for diagnosis and prognosis from omics data, feature selection has become an indispensable step to find a parsimonious set of informative features. However, many previous studies report considerabl...
متن کاملFeature Selection For Gene Selection And Prediction
In many machine learning applications, one must perform feature selection in order to obtain good classification performance. For example, selecting a good feature subset is critical when the sample size is small compared with the dimesionality and noise in the observations. When this is the case, it is necessary to reduce the number of features to avoid modeling noise in the classifier. When t...
متن کاملFeature Selection and Boosting Methods for Prediction of Cognitive Load from Acoustic Data
An analysis of acoustic features for a ternary cognitive load classification task and an application of a classification boosting method to the same task are presented. The analysis is based on a data set that encompasses a rich array of acoustic features as well as electroglottographic (EGG) data gathered for the COMputational PARalinguistic ChallengE (ComParE 2014). Supervised and unsupervise...
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 and Computing
سال: 2009
ISSN: 0960-3174,1573-1375
DOI: 10.1007/s11222-009-9148-5