Haithem Taha Mohammad Ali

Bio: Haithem Taha Mohammad Ali is an academic researcher from University of Kurdistan. The author has contributed to research in topics: Lasso (statistics) & Quantile regression. The author has an hindex of 8, co-authored 19 publications receiving 200 citations.

The Bayesian adaptive lasso regression

Abstract: Classical adaptive lasso regression is known to possess the oracle properties; namely, it performs as well as if the correct submodel were known in advance. However, it requires consistent initial estimates of the regression coefficients, which are generally not available in high dimensional settings. In addition, none of the algorithms used to obtain the adaptive lasso estimators provide a valid measure of standard error. To overcome these drawbacks, some Bayesian approaches have been proposed to obtain the adaptive lasso and related estimators. In this paper, we consider a fully Bayesian treatment for the adaptive lasso that leads to a new Gibbs sampler with tractable full conditional posteriors. Through simulations and real data analyses, we compare the performance of the new Gibbs sampler with some of the existing Bayesian and non-Bayesian methods. Results show that the new approach performs well in comparison to the existing Bayesian and non-Bayesian approaches.

A QSAR classification model for neuraminidase inhibitors of influenza A viruses (H1N1) based on weighted penalized support vector machine.

Abstract: Descriptor selection is a procedure widely used in chemometrics The aim is to select the best subset of descriptors relevant to the quantitative structure-activity relationship (QSAR) study being considered In this paper, a new descriptor selection method for the QSAR classification model is proposed by adding a new weight inside L1-norm The experimental results from classifying the neuraminidase inhibitors of influenza A viruses (H1N1) demonstrate that the proposed method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance and the number of selected descriptors

Bayesian quantile regression for ordinal longitudinal data

Abstract: Since the pioneering work by Koenker and Bassett [27], quantile regression models and its applications have become increasingly popular and important for research in many areas. In this paper, a random effects ordinal quantile regression model is proposed for analysis of longitudinal data with ordinal outcome of interest. An efficient Gibbs sampling algorithm was derived for fitting the model to the data based on a location-scale mixture representation of the skewed double-exponential distribution. The proposed approach is illustrated using simulated data and a real data example. This is the first work to discuss quantile regression for analysis of longitudinal data with ordinal outcome.

The Bayesian adaptive lasso regression

Abstract: Classical adaptive lasso regression is known to possess the oracle properties; namely, it performs as well as if the correct submodel were known in advance. However, it requires consistent initial estimates of the regression coefficients, which are generally not available in high dimensional settings. In addition, none of the algorithms used to obtain the adaptive lasso estimators provide a valid measure of standard error. To overcome these drawbacks, some Bayesian approaches have been proposed to obtain the adaptive lasso and related estimators. In this paper, we consider a fully Bayesian treatment for the adaptive lasso that leads to a new Gibbs sampler with tractable full conditional posteriors. Through simulations and real data analyses, we compare the performance of the new Gibbs sampler with some of the existing Bayesian and non-Bayesian methods. Results show that the new approach performs well in comparison to the existing Bayesian and non-Bayesian approaches.

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Abstract: Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

A Hybrid Gene Selection Method Based on ReliefF and Ant Colony Optimization Algorithm for Tumor Classification.

Abstract: For the DNA microarray datasets, tumor classification based on gene expression profiles has drawn great attention, and gene selection plays a significant role in improving the classification performance of microarray data. In this study, an effective hybrid gene selection method based on ReliefF and Ant colony optimization (ACO) algorithm for tumor classification is proposed. First, for the ReliefF algorithm, the average distance among k nearest or k non-nearest neighbor samples are introduced to estimate the difference among samples, based on which the distances between the samples in the same class or the different classes are defined, and then it can more effectively evaluate the weight values of genes for samples. To obtain the stable results in emergencies, a distance coefficient is developed to construct a new formula of updating weight coefficient of genes to further reduce the instability during calculations. When decreasing the distance between the same samples and increasing the distance between the different samples, the weight division is more obvious. Thus, the ReliefF algorithm can be improved to reduce the initial dimensionality of gene expression datasets and obtain a candidate gene subset. Second, a new pruning rule is designed to reduce dimensionality and obtain a new candidate subset with the smaller number of genes. The probability formula of the next point in the path selected by the ants is presented to highlight the closeness of the correlation relationship between the reaction variables. To increase the pheromone concentration of important genes, a new phenotype updating formula of the ACO algorithm is adopted to prevent the pheromone left by the ants that are overwhelmed with time, and then the weight coefficients of the genes are applied here to eliminate the interference of difference data as much as possible. It follows that the improved ACO algorithm has the ability of the strong positive feedback, which quickly converges to an optimal solution through the accumulation and the updating of pheromone. Finally, by combining the improved ReliefF algorithm and the improved ACO method, a hybrid filter-wrapper-based gene selection algorithm called as RFACO-GS is proposed. The experimental results under several public gene expression datasets demonstrate that the proposed method is very effective, which can significantly reduce the dimensionality of gene expression datasets, and select the most relevant genes with high classification accuracy.