scholarly journals Performance Analysis of Classification Methods and Alternative Linear Programming Integrated with Fuzzy Delphi Feature Selection

2013 ◽  
Vol 5 (10) ◽  
pp. 9-20 ◽  
Author(s):  
Bahram Izadi ◽  
Bahram Ranjbarian ◽  
Saeedeh Ketabi ◽  
Faria Nassiri-Mofakham
Keyword(s):  
Feature Selection ◽  
Linear Programming ◽  
Performance Analysis ◽  
Classification Methods ◽  
Fuzzy Delphi

scholarly journals A Review on Time-domain Peak Detection and Classification Algorithms for Electroencephalogram Signals

Mekatronika ◽  
2019 ◽  
Vol 1 (2) ◽  
pp. 115-121
Author(s):  
Asrul Adam ◽  
Ammar Faiz Zainal Abidin ◽  
Zulkifli Md Yusof ◽  
Norrima Mokhtar ◽  
Mohd Ibrahim Shapiai
Keyword(s):  
Feature Selection ◽  
Time Domain ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Peak Detection ◽  
Classification Algorithm ◽  
Classification Algorithms ◽  
Classification Methods ◽  
Eeg Signals ◽  
Selection Algorithms

In this paper, the developments in the field of EEG signals peaks detection and classification methods based on time-domain analysis have been discussed. The use of peak classification algorithm has end up the most significant approach in several applications. Generally, the peaks detection and classification algorithm is a first step in detecting any event-related for the variation of signals. A review based on the variety of peak models on their respective classification methods and applications have been investigated. In addition, this paper also discusses on the existing feature selection algorithms in the field of peaks classification.

Statistical Analysis of High-Level Features from State of the Union Addresses

2017 ◽  
Vol 8 (2) ◽  
pp. 50-73 ◽  
Author(s):  
Trevor J. Bihl ◽  
Kenneth W. Bauer Jr.
Keyword(s):  
Logistic Regression ◽  
Feature Selection ◽  
Statistical Analysis ◽  
Classification Methods ◽  
State Of The Union ◽  
Recent Interest ◽  
Science Approach ◽  
High Level ◽  
Statistical Clustering ◽  
Delivery Medium

A computational political science approach is taken to analyze the State of the Union Addresses (SUA) from 1790 to 2015. While low-level features, e.g. linguistic characteristics, are commonly used for lexical analysis, the authors herein illustrate the utility of high-level features, e.g. Flesch-Kincaid readability, for knowledge discovery and discrimination between types of speeches. A process is developed and employed to exploit high-level features which employs 1) statistical clustering (k-means) and a literature review to define types of speeches (e.g. written or oral), 2) classification methods via logistic regression to examine the validity of the defined classes, and 3) classifier-based feature selection to determine salient features. Recent interest in the SUA has posited that changes in readability in the SUA are due to declining audience capabilities; however, the authors' results show that changes in readability are a reflection of changes in the SUA delivery medium.

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