Combining heterogeneous classifiers for stock selection
نویسندگان
چکیده
منابع مشابه
Combining heterogeneous classifiers for stock selection
Combining unbiased forecasts of continuous variables necessarily reduces the error variance below that of the median individual forecast. However, this does not necessarily hold for forecasts of discrete variables, or where the costs of errors are not directly related to the error variance. This paper investigates empirically the benefits of combining forecasts of outperforming shares, based on...
متن کاملCombining heterogeneous classifiers for relational databases
Most enterprise data is distributed in multiple relational databases with expert-designed schema. Using traditional single-table machine learning techniques over such data not only incur a computational penalty for converting to a flat form (mega-join), even the human-specified semantic information present in the relations is lost. In this paper, we present a practical, twophase hierarchical me...
متن کاملMethods for Combining Heterogeneous Sets of Classifiers
The combination of classifiers has long been proposed as a method to improve the accuracy achieved in isolation by a single classifier. In contrast to such wellexplored methods as boosting and bagging, we are interested in ensemble methods that allow the combination of heterogeneous sets of classifiers, which are classifiers built using differing learning paradigms. We focus on theoretical and ...
متن کاملCombining Heterogeneous Classifiers for Word Sense Disambiguation
This paper discusses ensembles of simple but heterogeneous classifiers for word-sense disambiguation, examining the Stanford-CS224N system entered in the SENSEVAL-2 English lexical sample task. First-order classifiers are combined by a second-order classifier, which variously uses majority voting, weighted voting, or a maximum entropy model. While individual first-order classifiers perform comp...
متن کاملCombining Heterogeneous Classifiers for Network Intrusion Detection
Extensive use of computer networks and online electronic data and high demand for security has called for reliable intrusion detection systems. A repertoire of different classifiers has been proposed for this problem over last decade. In this paper we propose a combining classification approach for intrusion detection. Outputs of four base classifiers ANN, SVM, kNN and decision trees are fused ...
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: Intelligent Systems in Accounting, Finance and Management
سال: 2007
ISSN: 1055-615X,1099-1174
DOI: 10.1002/isaf.282