quote

LaTex

@article{JIMENEZ201775,
title = “Multi-objective evolutionary feature selection for online sales forecasting”,
journal = “Neurocomputing”,
volume = “234”,
pages = “75 - 92”,
year = “2017”,
issn = “0925-2312”,
doi = “https://doi.org/10.1016/j.neucom.2016.12.045“,
url = “http://www.sciencedirect.com/science/article/pii/S0925231216315612“,
author = “F. Jim茅nez and G. S谩nchez and J.M. Garc铆a and G. Sciavicco and L. Miralles”,
keywords = “Multi-objective evolutionary algorithms, Feature selection, Random forest, Regression model, Online sales forecasting”
}

Normal

F. Jiménez, G. Sánchez, J.M. García, G. Sciavicco, L. Miralles,
Multi-objective evolutionary feature selection for online sales forecasting,
Neurocomputing,
Volume 234,
2017,
Pages 75-92,
ISSN 0925-2312,
https://doi.org/10.1016/j.neucom.2016.12.045.
(http://www.sciencedirect.com/science/article/pii/S0925231216315612)
Keywords: Multi-objective evolutionary algorithms; Feature selection; Random forest; Regression model; Online sales forecasting

Summary

historical sales figures
products characteristics and peculiarities
sound financial and business plans

an accurate regression model for online sales forecasting：
a novel feature selection methodology
multi-objective evolutionary algorithm
ENORA (Evolutionary NOn-dominated Radial slots based Algorithm)
a wrapper method
regression model learner — Random Forest

integrates feature selection for regression, model evaluation, and decision making
in order to choose the most satisfactory model
an a posteriori process
a multi-objective context

main content

root mean squared error (RMSE)

ENORA (Evolutionary NOn-dominated Radial slots based Algorithm)

a (μ + λ) survival strategy
an elitist method
μ = λ = N
N is the size of the population,
binary tournament selection,
and self-adaptive crossover and mutation
for multi-objective evolutionary optimization

a rank-crowding-better function

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$d = \left\lfloor \sqrt[n-1]N \right\rfloor$
$h_j^I$ — Objective function $f_j^I$ exist $[0,1]$ After normalization
$n$ — number of objective functions

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NSGA-II (Non-dominated Sorted Genetic Algorithm)

a (μ + λ) strategy
a binary tournament selection
a rank-crowding better function

Difference between ENORA and NSGA-II

how the calculation of the ranking of the individuals in the population is performed

ENORA：the non-domination level of the individual in its slot
NSGA-II：the non-domination level of the individual in the whole population

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In the binary tournament, can the dominant individual win? Can
individual C be better than B to improve diversity

Feature selection

算法：
- supervised
- unsupervised
- semi-supervised

Depends on whether the training set is labeled

模型：
- filter — statistical measures
- wrapper — a search problem
- embedded — model-dependent

算法步骤：
- subset generation — greedy hill-climbing approach, sequential forward selection, sequential backward elimination, bi-directional selection, branch and bound, beam search, Las Vegas algorithms, evolutionary algorithms, and particle swarm optimization algorithms.
- subset evaluation — multivariate filter methods (the distance, the uncertainty, the dependence, and the consistency) + wrapper methods (the accuracy)
- stopping criterion
- result validation