Experimental Design for Variable Selection in data bases

This paper analyses the influence of 13 stylized facts of the German economy on the West German business cycles from 1955 to 1994. The method used in this investigation is Statistical Experimental Design with orthogonal factors. We are looking for all existing Plackett-Burman designs realizable by coded observations of these data. The plans are then analysed by regression with forward selection and various classification methods to extract the relevant variables for separating upswing and downswing of the cycles. The results are compared with already existing studies on this topic. --

Global gene expression analysis provides insight into local adaptation to geothermal streams in tadpoles of the Andean toad Rhinella spinulosa

Indexación: Web of Science; Scopus.The anuran Rhinella spinulosa is distributed along the Andes Range at altitudes that undergo wide daily and seasonal variation in temperature. One of the populations inhabits geothermal streams, a stable environment that influences life history traits such as the timing of metamorphosis. To investigate whether this population has undergone local adaptation to this unique habitat, we carried out transcriptome analyses in animals from two localities in two developmental stages (prometamorphic and metamorphic) and exposed them to two temperatures (20 and 25 degrees C). RNA-Seq, de novo assembly and annotation defined a transcriptome revealing 194,469 high quality SNPs, with 1,507 genes under positive selection. Comparisons among the experimental conditions yielded 1,593 differentially expressed genes. A bioinformatics search for candidates revealed a total of 70 genes that are highly likely to be implicated in the adaptive response of the population living in a stable environment, compared to those living in an environment with variable temperatures. Most importantly, the population inhabiting the geothermal environment showed decreased transcriptional plasticity and reduced genetic variation compared to its counterpart from the non-stable environment. This analysis will help to advance the understanding of the molecular mechanisms that account for the local adaptation to geothermal streams in anurans.https://www.nature.com/articles/s41598-017-01982-

Model structure selection using an integrated forward orthogonal search algorithm interfered with squared correlation and mutual information

Model structure selection plays a key role in nonlinear system identification. The first step in nonlinear system identification is to determine which model terms should be included in the model. Once significant model terms have been determined, a model selection criterion can then be applied to select a suitable model subset. The well known orthogonal least squares type algorithms are one of the most efficient and commonly used techniques for model structure selection. However, it has been observed that the orthogonal least squares type algorithms may occasionally select incorrect model terms or yield a redundant model subset in the presence of particular noise structures or input signals. A very efficient integrated forward orthogonal searching (IFOS) algorithm, which is interfered with squared correlation and mutual information, and which incorporates a general cross-validation (GCV) criterion and hypothesis tests, is introduced to overcome these limitations in model structure selection

Model structure selection using an integrated forward orthogonal search algorithm assisted by squared correlation and mutual information

Model structure selection plays a key role in non-linear system identification. The first step in non-linear system identification is to determine which model terms should be included in the model. Once significant model terms have been determined, a model selection criterion can then be applied to select a suitable model subset. The well known Orthogonal Least Squares (OLS) type algorithms are one of the most efficient and commonly used techniques for model structure selection. However, it has been observed that the OLS type algorithms may occasionally select incorrect model terms or yield a redundant model subset in the presence of particular noise structures or input signals. A very efficient Integrated Forward Orthogonal Search (IFOS) algorithm, which is assisted by the squared correlation and mutual information, and which incorporates a Generalised Cross-Validation (GCV) criterion and hypothesis tests, is introduced to overcome these limitations in model structure selection

Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al. \cite{LARS}), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: 1) the local geometry of samples is well preserved for low dimensional data representation, 2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, 3) the projection matrix of MEN improves the parsimony in computation, 4) the elastic net penalty reduces the over-fitting problem, and 5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.Comment: 33 pages, 12 figure

A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis

Prediction problems from spectra are largely encountered in chemometry. In addition to accurate predictions, it is often needed to extract information about which wavelengths in the spectra contribute in an effective way to the quality of the prediction. This implies to select wavelengths (or wavelength intervals), a problem associated to variable selection. In this paper, it is shown how this problem may be tackled in the specific case of smooth (for example infrared) spectra. The functional character of the spectra (their smoothness) is taken into account through a functional variable projection procedure. Contrarily to standard approaches, the projection is performed on a basis that is driven by the spectra themselves, in order to best fit their characteristics. The methodology is illustrated by two examples of functional projection, using Independent Component Analysis and functional variable clustering, respectively. The performances on two standard infrared spectra benchmarks are illustrated.Comment: A paraitr

On-line multiobjective automatic control system generation by evolutionary algorithms

Evolutionary algorithms are applied to the on- line generation of servo-motor control systems. In this paper, the evolving population of controllers is evaluated at run-time via hardware in the loop, rather than on a simulated model. Disturbances are also introduced at run-time in order to pro- duce robust performance. Multiobjective optimisation of both PI and Fuzzy Logic controllers is considered. Finally an on-line implementation of Genetic Programming is presented based around the Simulink standard blockset. The on-line designed controllers are shown to be robust to both system noise and ex- ternal disturbances while still demonstrating excellent steady- state and dvnamic characteristics

Parallel Hybrid Trajectory Based Metaheuristics for Real-World Problems

G. Luque, E. Alba, Parallel Hybrid Trajectory Based Metaheuristics for Real-World Problems, In Proceedings of Intelligent Networking and Collaborative Systems, pp. 184-191, 2-4 September, 2015, Taipei, Taiwan, IEEE PressThis paper proposes a novel algorithm combining path relinking with a set of cooperating trajectory based parallel algorithms to yield a new metaheuristic of enhanced search features. Algorithms based on the exploration of the neighborhood of a single solution, like simulated annealing (SA), have offered accurate results for a large number of real-world problems in the past. Because of their trajectory based nature, some advanced models such as the cooperative one are competitive in academic problems, but still show many limitations in addressing large scale instances. In addition, the field of parallel models for trajectory methods has not deeply been studied yet (at least in comparison with parallel population based models). In this work, we propose a new hybrid algorithm which improves cooperative single solution techniques by using path relinking, allowing both to reduce the global execution time and to improve the efficacy of the method. We applied here this new model using a large benchmark of instances of two real-world NP-hard problems: DNA fragment assembly and QAP problems, with competitive results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech