Two-Stage Classification with SIS Using a New Filter Ranking Method in High Throughput Data

Improving the classification performance with group lasso-based ranking method in high dimensional correlated data

The high-throughput correlated DNA methylation (DNAmeth) dataset generated from Illumina Infinium Human Methylation 27 (IIHM 27K) BeadChip assay. In the DNAmeth data, there are several CpG sites for every gene, and these grouped CpG sites are highly correlated. Most of the current filtering-based ranking (FBR) methods do not consider the group correlation structures. Obtaining the significant features with the FBR methods and applying these features to the classifiers to attain the best classification accuracy in highly correlated DNAmeth data is a challenging task. In this research, we introduce a resampling of group least absolute shrinkage and selection operator (glasso) FBR method capable of ignoring the unrelated features in the data considering the group correlation among the features. The various classifiers, such as random forests (RF), Naive Bayes (NB), and support vector machines (SVM) with the significant CpGs obtained from the proposed resampling of group lasso-based ranking (RGLR) method helped to boost the classification accuracy. Through simulated and experimental prostate DNAmeth data, we showed that higher performance of accuracy, sensitivity, specificity, and geometric mean is achieved by ignoring the unimportant CpG sites through the RGLR method.

Adaptive lasso with weights based on normalized filtering scores in molecular big data

The molecular big data are highly correlated, and numerous genes are not related. The various classification methods performance mainly rely on the selection of significant genes. Sparse regularized regression (SRR) models using the least absolute shrinkage and selection operator (lasso) and adaptive lasso (alasso) are popularly used for gene selection and classification. Nevertheless, it becomes challenging when the genes are highly correlated. Here, we propose a modified adaptive lasso with weights using the ranking-based feature selection (RFS) methods capable of dealing with the highly correlated gene expression data. Firstly, an RFS methods such as Fisher’s score (FS), Chi-square (CS), and information gain (IG) are employed to ignore the unimportant genes and the top significant genes are chosen through sure independence screening (SIS) criteria. The scores of the ranked genes are normalized and assigned as proposed weights to the alasso method to obtain the most significant genes that were proven to be biologically related to the cancer type and helped in attaining higher classification performance. With the synthetic data and real application of microarray data, we demonstrated that the proposed alasso method with RFS methods is a better approach than the other known methods such as alasso with filtering such as ridge and marginal maximum likelihood estimation (MMLE), lasso and alasso without filtering. The metrics of accuracy, area under the receiver operating characteristics curve (AUROC), and geometric mean (GM-mean) are used for evaluating the performance of the models.

Combination of Ensembles of Regularized Regression Models with Resampling-Based Lasso Feature Selection in High Dimensional Data

In high-dimensional data, the performances of various classifiers are largely dependent on the selection of important features. Most of the individual classifiers with the existing feature selection (FS) methods do not perform well for highly correlated data. Obtaining important features using the FS method and selecting the best performing classifier is a challenging task in high throughput data. In this article, we propose a combination of resampling-based least absolute shrinkage and selection operator (LASSO) feature selection (RLFS) and ensembles of regularized regression (ERRM) capable of dealing data with the high correlation structures. The ERRM boosts the prediction accuracy with the top-ranked features obtained from RLFS. The RLFS utilizes the lasso penalty with sure independence screening (SIS) condition to select the top k ranked features. The ERRM includes five individual penalty based classifiers: LASSO, adaptive LASSO (ALASSO), elastic net (ENET), smoothly clipped absolute deviations (SCAD), and minimax concave penalty (MCP). It was built on the idea of bagging and rank aggregation. Upon performing simulation studies and applying to smokers’ cancer gene expression data, we demonstrated that the proposed combination of ERRM with RLFS achieved superior performance of accuracy and geometric mean.

Analyzing high dimensional correlated data using feature ranking and classifiers

The Illumina Infinium HumanMethylation27 (Illumina 27K) BeadChip assay is a relatively recent high-throughput technology that allows over 27,000 CpGs to be assayed. The Illumina 27K methylation data is less commonly used in comparison to gene expression in bioinformatics. It provides a critical need to find the optimal feature ranking (FR) method for handling the high dimensional data. The optimal FR method on the classifier is not well known, and choosing the best performing FR method becomes more challenging in high dimensional data setting. Therefore, identifying the statistical methods which boost the inference is of crucial importance in this context. This paper describes the detailed performances of FR methods such as fisher score, information gain, chi-square, and minimum redundancy and maximum relevance on different classification methods such as Adaboost, Random Forest, Naive Bayes, and Support Vector Machines. Through simulation study and real data applications, we show that the fisher score as an FR method, when applied on all the classifiers, achieved best prediction accuracy with significantly small number of ranked features.