LOCUST: An Online Analytical Processing Framework for High Dimensional Classification of Data Streams

2008 ◽  
Author(s):  
Charu C. Aggarwal ◽  
Philip S. Yu
Keyword(s):  
Data Streams ◽  
Online Analytical Processing ◽  
High Dimensional ◽  
Dimensional Classification ◽  
Analytical Processing ◽  
Processing Framework

Classification of high-dimensional evolving data streams via a resource-efficient online ensemble

2017 ◽  
Vol 31 (5) ◽  
pp. 1242-1265 ◽  
Author(s):  
Tingting Zhai ◽  
Yang Gao ◽  
Hao Wang ◽  
Longbing Cao
Keyword(s):  
Data Streams ◽  
High Dimensional ◽  
Evolving Data

A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks

2004 ◽  
Vol 3 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Markus Ruschhaupt ◽  
Wolfgang Huber ◽  
Annemarie Poustka ◽  
Ulrich Mansmann
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Statistical Processing ◽  
Primary Data ◽  
High Dimensional ◽  
Microarray Gene Expression ◽  
Data Set ◽  
Dimensional Classification ◽  
Biological Interpretation

We demonstrate a concept and implementation of a compendium for the classification of high-dimensional data from microarray gene expression profiles. A compendium is an interactive document that bundles primary data, statistical processing methods, figures, and derived data together with the textual documentation and conclusions. Interactivity allows the reader to modify and extend these components. We address the following questions: how much does the discriminatory power of a classifier depend on the choice of the algorithm that was used to identify it; what alternative classifiers could be used just as well; how robust is the result. The answers to these questions are essential prerequisites for validation and biological interpretation of the classifiers. We show how to use this approach by looking at these questions for a specific breast cancer microarray data set that first has been studied by Huang et al. (2003).

scholarly journals High Dimensional Classification of Structural MRI Alzheimer?s Disease Data Based on Large Scale Regularization

2011 ◽  
Vol 5 ◽  
Author(s):  
Ramon Casanova ◽  
Christopher T. Whitlow ◽  
Benjamin Wagner ◽  
Jeff Williamson ◽  
Sally A. Shumaker ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Mri ◽  
High Dimensional ◽  
Dimensional Classification

Diagnoses, Dimensions, and DSM-5

2010 ◽  
pp. 254-279
Author(s):  
Thomas A. Widiger ◽  
Maryanne Edmundson
Keyword(s):  
Substance Use ◽  
Mental Disorders ◽  
Paradigm Shift ◽  
Empirical Support ◽  
Statistical Manual ◽  
Diagnostic And Statistical Manual ◽  
Dsm 5 ◽  
Dimensional Classification ◽  
Diagnosis And Classification

The Diagnostic and Statistical Manual of Mental Disorders, Third Edition (DSM-III) is often said to have provided a significant paradigm shift in how psychopathology is diagnosed. The authors of DSM-5 have the empirical support and the opportunity to lead the field of psychiatry to a comparably bold new future in diagnosis and classification. The purpose of this chapter is to address the validity of the categorical and dimensional models for the classification and diagnosis of psychopathology. Considered in particular will be research concerning substance use disorders, mood disorders, and personality disorders. Limitations and concerns with respect to a dimensional classification of psychopathology are also considered. The chapter concludes with a recommendation for a conversion to a more quantitative, dimensional classification of psychopathology.

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

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