Class Centroid Method for Variable Selection and Classification in Metabolomics

2015 ◽  
Vol 13 (11) ◽  
pp. 980-985
Author(s):  
Wei Kong ◽  
Xin Xie ◽  
Qi Shen ◽  
Weimin Shi
Keyword(s):  
Variable Selection ◽  
Centroid Method ◽  
Class Centroid

scholarly journals IMPROVING DETECTION PERFORMANCE OF DUPLICATE BUG REPORTS USING EXTENDED CLASS CENTROID INFORMATION

2019 ◽  
Vol 1 (26) ◽  
pp. 71-79
Author(s):  
Phuc Minh Nhan
Keyword(s):  
Software Maintenance ◽  
Software Projects ◽  
Centroid Method ◽  
Detection Scheme ◽  
Bug Reports ◽  
Software Packages ◽  
Extended Class ◽  
Detection Schemes ◽  
Class Centroid ◽  
Duplicate Bug Reports

In software maintenance, bug reports play an important role in the correctness of  software packages. Unfortunately, the duplicatebug report problem arises because there are too many duplicate bug reports in various software projects. Handling with duplicate bug reports is thus time-consuming and has high cost of software maintenance. Therefore, this research introduces a detection scheme based on the extended class centroid information (ECCI) to enhance thedetection performance. This method is extended from the previous one, which used only centroid method without considering the effects of both inner and inter class. Besides, this method also improved the previous use of normalized cosine in identifying the similarity between two bug reports by denormalized cosine.  The effectiveness of ECCI is proved through the empirical study with three open-source projects: SVN, Argo UML and Apache. The experimental results show thatECCI outperforms other detection schemes by about 10% in all cases.

Polarity and Variable Selection Heuristics for SAT-Based Anytime MaxSAT

2020 ◽  
Vol 12 (1) ◽  
pp. 17-22
Author(s):  
Alexander Nadel
Keyword(s):  
Variable Selection ◽  
Open Source ◽  
System Description

This paper is a system description of the anytime MaxSAT solver TT-Open-WBO-Inc, which won both of the weighted incomplete tracks of MaxSAT Evaluation 2019. We implemented the recently introduced polarity and variable selection heuristics, TORC and TSB, respectively, in the Open-WBO-Inc-BMO algorithm within the open-source anytime MaxSAT solver Open-WBO-Inc. As a result, the solver is substantially more efficient.

Database-Driven Modeling based on Variable Selection using Random Forest and Its Application for Linear Air Fuel Ratio Sensor Output Prediction

2019 ◽  
Vol 139 (8) ◽  
pp. 850-857
Author(s):  
Hiromu Imaji ◽  
Takuya Kinoshita ◽  
Toru Yamamoto ◽  
Keisuke Ito ◽  
Masahiro Yoshida ◽  
...  
Keyword(s):  
Random Forest ◽  
Variable Selection ◽  
Sensor Output ◽  
Fuel Ratio

Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain

2019 ◽  
Author(s):  
Sierra Bainter ◽  
Thomas Granville McCauley ◽  
Tor D Wager ◽  
Elizabeth Reynolds Losin
Keyword(s):  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Limited Information ◽  
Online Application ◽  
Stochastic Search Variable Selection ◽  
Selection Approach ◽  
Pain Ratings ◽  
Research Questions ◽  
Standard Techniques ◽  
Search Variable

In this paper we address the problem of selecting important predictors from some larger set of candidate predictors. Standard techniques are limited by lack of power and high false positive rates. A Bayesian variable selection approach used widely in biostatistics, stochastic search variable selection, can be used instead to combat these issues by accounting for uncertainty in the other predictors of the model. In this paper we present Bayesian variable selection to aid researchers facing this common scenario, along with an online application (https://ssvsforpsych.shinyapps.io/ssvsforpsych/) to perform the analysis and visualize the results. Using an application to predict pain ratings, we demonstrate how this approach quickly identifies reliable predictors, even when the set of possible predictors is larger than the sample size. This technique is widely applicable to research questions that may be relatively data-rich, but with limited information or theory to guide variable selection.

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