Failure prediction of Indian Banks using SMOTE, Lasso regression, bagging and boosting
نویسندگان
چکیده
منابع مشابه
Combining Bagging, Boosting and Random Subspace Ensembles for Regression Problems
Bagging, boosting and random subspace methods are well known re-sampling ensemble methods that generate and combine a diversity of learners using the same learning algorithm for the base-regressor. In this work, we built an ensemble of bagging, boosting and random subspace methods ensembles with 8 sub-regressors in each one and then an averaging methodology is used for the final prediction. We ...
متن کاملBagging and Boosting performance in Projection Pursuit Regression
Recently, many authors have proposed new algorithms to improve the accuracy of certain classifiers on artificial and real data sets. The goal is to assemble a collection of individual classifiers based on resampling of data set. Bagging (Breiman, 1996) and AdaBoost (Freund & Schapire, 1997) are the most used procedures: the first fits many classifiers to bootstrap samples of data and classifies...
متن کاملWriter Demographic Classification Using Bagging and Boosting
Classifying handwriting into a writer demographic category, e.g., gender, age, or handedness of the writer, is useful for more detailed analysis such as writer verification and identification. This paper describes classification into binary demographic categories using document macro features and several different classification methods: a single feed-forward neural network classifier and combi...
متن کاملBagging, Boosting, and C4.5
Breiman's bagging and Freund and Schapire's boosting are recent methods for improving the predictive power of classiier learning systems. Both form a set of classiiers that are combined by voting, bagging by generating replicated boot-strap samples of the data, and boosting by adjusting the weights of training instances. This paper reports results of applying both techniques to a system that le...
متن کاملCombining Bagging and Boosting
Bagging and boosting are among the most popular resampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the base-classifiers. Boosting algorithms are considered stronger than bagging on noisefree data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, i...
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: Cogent Economics & Finance
سال: 2020
ISSN: 2332-2039
DOI: 10.1080/23322039.2020.1729569