Feature subset selection and feature ranking for multivariate time series
نویسندگان
چکیده
منابع مشابه
A Supervised Feature Subset Selection Technique for Multivariate Time Series
Feature subset selection (FSS) is a known technique to pre-process the data before performing any data mining tasks, e.g., classification and clustering. FSS provides both cost-effective predictors and a better understanding of the underlying process that generated data. We propose Corona, a simple yet effective supervised feature subset selection technique for Multivariate Time Series (MTS). T...
متن کاملSingle Feature Ranking and Binary Particle Swarm Optimisation Based Feature Subset Ranking for Feature Selection
This paper proposes two wrapper based feature selection approaches, which are single feature ranking and binary particle swarm optimisation (BPSO) based feature subset ranking. In the first approach, individual features are ranked according to the classification accuracy so that feature selection can be accomplished by using only a few top-ranked features for classification. In the second appro...
متن کاملA New Framework for Distributed Multivariate Feature Selection
Feature selection is considered as an important issue in classification domain. Selecting a good feature through maximum relevance criterion to class label and minimum redundancy among features affect improving the classification accuracy. However, most current feature selection algorithms just work with the centralized methods. In this paper, we suggest a distributed version of the mRMR featu...
متن کاملCL eVer: A Feature Subset Selection Technique for Multivariate Time Series
Feature subset selection (FSS) is one of the techniques to preprocess the data before performing any data mining tasks, e.g., classification and clustering. FSS provides both cost-effective predictors and a better understanding of the underlying process that generated data. We propose a novel method of FSS for Multivariate Time Series (MTS) based on Common Principal Component Analysis, termed C...
متن کاملAn Adaptive Multiple Feature Subset Method for Feature Ranking and Feature Selection
In this paper, we propose a new feature evaluation method that forms the basis for feature ranking and feature selection. The method starts by generating a number of feature subsets in a random fashion and evaluates features based on the derived subsets. It then proceeds in a number of stages. In each stage, it inputs the features whose ranks in the previous stage were above the median rank and...
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 Knowledge and Data Engineering
سال: 2005
ISSN: 1041-4347
DOI: 10.1109/tkde.2005.144