Chaotic Time Series Prediction with Feature Selection Evolution

Landassuri-Moreno, V.; Marcial‐Romero, J. Raymundo; Montes-Venegas, A.; Ramos, Marco A.

doi:10.1109/cerma.2011.19

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Chaotic time series with the EPNet algorithm is proposed in [ 27 ]. The authors present four different methods derived from the classical EPNet algorithm applied in three different chaotic series (Logistic, Lorenz, and Mackey-Glass).…”

Section: Introductionmentioning

confidence: 99%

Feature Selection via Chaotic Antlion Optimization

2016

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BackgroundSelecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting) while minimizing the number of features used.ResultsWe propose an optimization approach for the feature selection problem that considers a “chaotic” version of the antlion optimizer method, a nature-inspired algorithm that mimics the hunting mechanism of antlions in nature. The balance between exploration of the search space and exploitation of the best solutions is a challenge in multi-objective optimization. The exploration/exploitation rate is controlled by the parameter I that limits the random walk range of the ants/prey. This variable is increased iteratively in a quasi-linear manner to decrease the exploration rate as the optimization progresses. The quasi-linear decrease in the variable I may lead to immature convergence in some cases and trapping in local minima in other cases. The chaotic system proposed here attempts to improve the tradeoff between exploration and exploitation. The methodology is evaluated using different chaotic maps on a number of feature selection datasets. To ensure generality, we used ten biological datasets, but we also used other types of data from various sources. The results are compared with the particle swarm optimizer and with genetic algorithm variants for feature selection using a set of quality metrics.

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Section: Introductionmentioning

confidence: 99%

Feature Selection via Chaotic Antlion Optimization

2016

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“…Each individual in the population represents a candidate solution and the feature dimension is the same length as chromosomes length [ 18 , 19 ]. The chaotic time series with an EPNet algorithm requires a small network architecture whereas the expansion of neural parts might debase the performance amid the evolution and gives more survival probabilities to smaller networks in the population [ 20 , 21 ]. The chaotic antlion optimization (CALO) can converge to the same optimal solution for a higher number of applications regardless of the stochastic searching and the chaotic adjustment [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Impact of Chaos Functions on Modern Swarm Optimizers

Emary

Zawbaa

2016

PLoS ONE

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Exploration and exploitation are two essential components for any optimization algorithm. Much exploration leads to oscillation and premature convergence while too much exploitation slows down the optimization algorithm and the optimizer may be stuck in local minima. Therefore, balancing the rates of exploration and exploitation at the optimization lifetime is a challenge. This study evaluates the impact of using chaos-based control of exploration/exploitation rates against using the systematic native control. Three modern algorithms were used in the study namely grey wolf optimizer (GWO), antlion optimizer (ALO) and moth-flame optimizer (MFO) in the domain of machine learning for feature selection. Results on a set of standard machine learning data using a set of assessment indicators prove advance in optimization algorithm performance when using variational repeated periods of declined exploration rates over using systematically decreased exploration rates.

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