Selecting the minimal best subset out of a huge number of factors for influencing the response is a fundamental and very challenging NP-hard problem because the presence of many redundant genes results in over-fitting easily while missing an important gene can more detrimental impact on predictions, and computation is prohibitive for exhaust search. We propose a modified memetic algorithm (MA) based on an improved splicing method to overcome the problems in the traditional genetic algorithm exploitation capability and dimension reduction in the predictor variables. The new algorithm accelerates the search in identifying the minimal best subset of genes by incorporating it into the new local search operator and hence improving the splicing method. The improvement is also due to another two novel aspects: (a) updating subsets of genes iteratively until the no more reduction in the loss function by splicing and increasing the probability of selecting the true subsets of genes; and (b) introducing add and del operators based on backward sacrifice into the splicing method to limit the size of gene subsets. Additionally, according to the experimental results, our proposed optimizer can obtain a better minimal subset of genes with a few iterations, compared with all considered algorithms. Moreover, the mutation operator is replaced by it to enhance exploitation capability and initial individuals are improved by it to enhance efficiency of search. A dataset of the body weight of Hu sheep was used to evaluate the superiority of the modified MA against the genetic algorithm. According to our experimental results, our proposed optimizer can obtain a better minimal subset of genes with a few iterations, compared with all considered algorithms including the most advanced adaptive best-subset selection algorithm.

No abstract

In the stock market, accurate prediction of stock price movement direction can effectively increase the profits for investors. However, the stock price is an extremely complex dynamic system with strong fluctuation, proper selection of technical indicators can potentially improve the accuracy of the direction prediction. We propose a novel sparse least squares support vector machine (LSSVM) by combining recursive feature elimination (RFE) and Relief via a weight parameter. Specially, the benefit if this hybrid is three fold: (1) accounting for any intrinsic correlations among the features, (2) more effective prediction due to the sparse framework capable of removing some “noise” features completely; and (3) simultaneously select technical indicators according to the feature ranking and accounts for possible interactions and possible non-linear effects among the features. Three stock datasets from the liquor and spirits concept are analyzed to demonstrate the superiority of our proposed new framework providing sparse solutions resulting in more accurate predictions and higher returns among all seven considered classifiers.