Using training set selection methods to improve text mining on market prediction via news headlines

2015 ◽  
Author(s):  
Farzad Niknam ◽  
Aliakbar Niknafs
Keyword(s):  
Text Mining ◽  
Selection Methods ◽  
Training Set ◽  
Training Set Selection ◽  
News Headlines

Computational methods for training set selection and error assessment applied to catalyst design: guidelines for deciding which reactions to run first and which to run next

2021 ◽  
Author(s):  
Andrew F. Zahrt ◽  
Brennan T. Rose ◽  
William T. Darrow ◽  
Jeremy J. Henle ◽  
Scott E. Denmark
Keyword(s):  
In Silico ◽  
Subset Selection ◽  
Design Guidelines ◽  
Catalyst Design ◽  
Error Assessment ◽  
Selection Methods ◽  
Training Set ◽  
Training Set Selection ◽  
Catalyst Selection ◽  
Selection Of

Different subset selection methods are examined to guide catalyst selection in optimization campaigns. Error assessment methods are used to quantitatively inform selection of new catalyst candidates from in silico libraries of catalyst structures.

Use of Molecular Similarity Indices for QSAR Training Set Selection

1995 ◽  
Vol 3 (4) ◽  
pp. 279-292 ◽  
Author(s):  
I. T. Cousins ◽  
M. T. D. Cronin ◽  
J. C. Dearden ◽  
C. D. Watts
Keyword(s):  
Molecular Similarity ◽  
Training Set ◽  
Similarity Indices ◽  
Training Set Selection

Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment

2015 ◽  
Vol 42 (1) ◽  
pp. 306-324 ◽  
Author(s):  
Arman Khadjeh Nassirtoussi ◽  
Saeed Aghabozorgi ◽  
Teh Ying Wah ◽  
David Chek Ling Ngo
Keyword(s):  
Text Mining ◽  
Dimension Reduction ◽  
Reduction Algorithm ◽  
News Headlines

New Entropy Based Distance for Training Set Selection in Debt Portfolio Valuation

2012 ◽  
Vol 7 (2) ◽  
pp. 60-69
Author(s):  
Tomasz Kajdanowicz ◽  
Slawomir Plamowski ◽  
Przemyslaw Kazienko
Keyword(s):  
Machine Learning ◽  
Distance Measure ◽  
Prediction Performance ◽  
Training Set ◽  
Learning Tasks ◽  
Real Domain ◽  
Valuation Process ◽  
Proper Training ◽  
Training Set Selection ◽  
Portfolio Valuation

Choosing a proper training set for machine learning tasks is of great importance in complex domain problems. In the paper a new distance measure for training set selection is presented and thoroughly discussed. The distance between two datasets is computed using variance of entropy in groups obtained after clustering. The approach is validated using real domain datasets from debt portfolio valuation process. Eventually, prediction performance is examined.

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