Test-retest reliability and feature selection in physiological time series classification
نویسندگان
چکیده
Feature test-retest reliability is proposed as a useful criterion for the selection/exclusion of features in time series classification tasks. Three sets of physiological time series are examined, EEG and ECG recordings together with measurements of neck movement. Comparisons of reliability estimates from test-retest studies with measures of feature importance from classification tasks suggest that low reliability can be used to exclude irrelevant features prior to classifier training. By removing features with low reliability an unnecessary degradation of the classifier accuracy may be avoided.
منابع مشابه
Online Streaming Feature Selection Using Geometric Series of the Adjacency Matrix of Features
Feature Selection (FS) is an important pre-processing step in machine learning and data mining. All the traditional feature selection methods assume that the entire feature space is available from the beginning. However, online streaming features (OSF) are an integral part of many real-world applications. In OSF, the number of training examples is fixed while the number of features grows with t...
متن کاملA Real-Time Electroencephalography Classification in Emotion Assessment Based on Synthetic Statistical-Frequency Feature Extraction and Feature Selection
Purpose: To assess three main emotions (happy, sad and calm) by various classifiers, using appropriate feature extraction and feature selection. Materials and Methods: In this study a combination of Power Spectral Density and a series of statistical features are proposed as statistical-frequency features. Next, a feature selection method from pattern recognition (PR) Tools is presented to e...
متن کاملFeature selection using genetic algorithm for classification of schizophrenia using fMRI data
In this paper we propose a new method for classification of subjects into schizophrenia and control groups using functional magnetic resonance imaging (fMRI) data. In the preprocessing step, the number of fMRI time points is reduced using principal component analysis (PCA). Then, independent component analysis (ICA) is used for further data analysis. It estimates independent components (ICs) of...
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 کاملAn Improved K-Nearest Neighbor with Crow Search Algorithm for Feature Selection in Text Documents Classification
The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Computer methods and programs in biomedicine
دوره 105 1 شماره
صفحات -
تاریخ انتشار 2012