scholarly journals Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling

Sensors ◽  
2016 ◽  
Vol 16 (11) ◽  
pp. 1783 ◽  
Author(s):  
Fuqun Zhou ◽  
Aining Zhang
Keyword(s):  
Time Series ◽  
Random Forests ◽  
Supervised Classification ◽  
Data Transfer ◽  
Subset Selection ◽  
Optimal Subset ◽  
Sample Data ◽  
Selection Of

scholarly journals STPGA: Selection of training populations with a genetic algorithm

2017 ◽  
Author(s):  
Deniz Akdemir
Keyword(s):  
Genetic Algorithm ◽  
Variable Selection ◽  
Optimization Problems ◽  
Subset Selection ◽  
Breeding Population ◽  
Mixed Integer ◽  
Optimal Subset ◽  
Look Ahead ◽  
Selection Of ◽  
The Ideal

AbstractOptimal subset selection is an important task that has numerous algorithms designed for it and has many application areas. STPGA contains a special genetic algorithm supplemented with a tabu memory property (that keeps track of previously tried solutions and their fitness for a number of iterations), and with a regression of the fitness of the solutions on their coding that is used to form the ideal estimated solution (look ahead property) to search for solutions of generic optimal subset selection problems. I have initially developed the programs for the specific problem of selecting training populations for genomic prediction or association problems, therefore I give discussion of the theory behind optimal design of experiments to explain the default optimization criteria in STPGA, and illustrate the use of the programs in this endeavor. Nevertheless, I have picked a few other areas of application: supervised and unsupervised variable selection based on kernel alignment, supervised variable selection with design criteria, influential observation identification for regression, solving mixed integer quadratic optimization problems, balancing gains and inbreeding in a breeding population. Some of these illustrations pertain new statistical approaches.

Der Sommer und Herbst 2003 aus phänologischer Sicht | Summer and autumn 2003 from a phenological point of view

2004 ◽  
Vol 155 (5) ◽  
pp. 142-145 ◽  
Author(s):  
Claudio Defila
Keyword(s):  
Time Series ◽  
New Record ◽  
Point Of View ◽  
Late Spring ◽  
Mean Deviation ◽  
Leaf Shedding ◽  
The Mean ◽  
Very High ◽  
Selection Of

The record-breaking heatwave of 2003 also had an impact on the vegetation in Switzerland. To examine its influences seven phenological late spring and summer phases were evaluated together with six phases in the autumn from a selection of stations. 30% of the 122 chosen phenological time series in late spring and summer phases set a new record (earliest arrival). The proportion of very early arrivals is very high and the mean deviation from the norm is between 10 and 20 days. The situation was less extreme in autumn, where 20% of the 103 time series chosen set a new record. The majority of the phenological arrivals were found in the class «normal» but the class«very early» is still well represented. The mean precocity lies between five and twenty days. As far as the leaf shedding of the beech is concerned, there was even a slight delay of around six days. The evaluation serves to show that the heatwave of 2003 strongly influenced the phenological events of summer and spring.

Selection of Neural Network for Crime Time Series Prediction by Virtual Leave-One-Out Tests

2019 ◽  
pp. 117-133
Author(s):  
Stanisław Jankowski ◽  
Zbigniew Szymański ◽  
Zbigniew Wawrzyniak ◽  
Paweł Cichosz ◽  
Eliza Szczechla ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Prediction ◽  
Virtual Leave One Out ◽  
Leave One Out ◽  
Selection Of

scholarly journals Training subset selection in Hourly Ontario Energy Price forecasting using time series clustering-based stratification

2015 ◽  
Vol 156 ◽  
pp. 268-279 ◽  
Author(s):  
Karol Lina López ◽  
Christian Gagné ◽  
Germán Castellanos-Dominguez ◽  
Mauricio Orozco-Alzate
Keyword(s):  
Time Series ◽  
Subset Selection ◽  
Energy Price ◽  
Price Forecasting ◽  
Time Series Clustering ◽  
Training Subset
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