Redundancy Feature Selection Method Research Articles

An improved conditional relevance and weighted redundancy feature selection method for gene expression data

An improved conditional relevance and weighted redundancy feature selection method for gene expression data

Kernel Partial Least Squares Feature Selection Based on Maximum Weight Minimum Redundancy

Feature selection refers to a vital function in machine learning and data mining. The maximum weight minimum redundancy feature selection method not only considers the importance of features but also reduces the redundancy among features. However, the characteristics of various datasets are not identical, and thus the feature selection method should have different feature evaluation criteria for all datasets. Additionally, high-dimensional data analysis poses a challenge to enhancing the classification performance of the different feature selection methods. This study presents a kernel partial least squares feature selection method on the basis of the enhanced maximum weight minimum redundancy algorithm to simplify the calculation and improve the classification accuracy of high-dimensional datasets. By introducing a weight factor, the correlation between the maximum weight and the minimum redundancy in the evaluation criterion can be adjusted to develop an improved maximum weight minimum redundancy method. In this study, the proposed KPLS feature selection method considers the redundancy between the features and the feature weighting between any feature and a class label in different datasets. Moreover, the feature selection method proposed in this study has been tested regarding its classification accuracy on data containing noise and several datasets. The experimental findings achieved using different datasets explore the feasibility and effectiveness of the proposed method which can select an optimal feature subset and obtain great classification performance based on three different metrics when compared with other feature selection methods.

Feature Selection Method Based on Differential Correlation Information Entropy

Feature selection is one of the major aspects of pattern classification systems. In previous studies, Ding and Peng recognized the importance of feature selection and proposed a minimum redundancy feature selection method to minimize redundant features for sequential selection in microarray gene expression data. However, since the minimum redundancy feature selection method is used mainly to measure the dependency between random variables of mutual information, the results cannot be optimal without consideration of global feature selection. Therefore, based on the framework of minimum redundancy-maximum correlation, this paper introduces entropy to measure global feature selection and proposes a new feature subset evaluation method, differential correlation information entropy. In our function, different bivariate correlation metrics are selected. Then, the feature selection is completed through sequence forward search. Two different classification models are used on eleven standard data sets of the UCI machine learning knowledge base to compare various comparison algorithms, such as mRMR, reliefF and feature selection method with joint maximal information entropy, with our method. The experimental results show that feature selection based on our proposed method is obviously superior to that of other models.

MLACO: A multi-label feature selection algorithm based on ant colony optimization

Nowadays, with emerge the multi-label datasets, the multi-label learning processes attracted interest and increasingly applied to different fields. In such learning processes, unlike single-label learning, instances have more than one class label simultaneously. Also, multi-label learning suffers from the curse of dimensionality, and thus, the feature selection becomes a difficult task. In this paper, we propose a novel multi-label relevance–redundancy feature selection method based on Ant colony optimization (ACO) for the first time, called MLACO. By introducing two unsupervised and supervised heuristic functions, MLACO tries to search in the features space to find the most promising features with the lowest redundancy (unsupervised) and highest relevancy with class labels (supervised) through several iterations. For speeding up the convergence of the algorithm, the normalized cosine similarity between features and class labels have been used as the initial pheromone of each ant. The proposed method does not take into account any learning algorithm, and it can be classified as a filter-based method. We compare the performance of the MLACO against five well-known and state-of-the-art feature selection methods using ML-KNN classifier. The experimental results on several frequently used datasets show the superiority of the MLACO in different multi-label evaluation measures criteria and runtime.

Non-small-cell lung cancer pathological subtype-related gene selection and bioinformatics analysis based on gene expression profiles.

Lung cancer is one of the most common malignant diseases and a major threat to public health on a global scale. Non-small-cell lung cancer (NSCLC) has a higher degree of malignancy and a lower 5-year survival rate compared with that of small-cell lung cancer. NSCLC may be mainly divided into two pathological subtypes, adenocarcinoma and squamous cell carcinoma. The aim of the present study was to identify disease genes based on the gene expression profile and the shortest path analysis of weighted functional protein association networks with the existing protein-protein interaction data from the Search Tool for the Retrieval of Interacting Genes. The gene expression profile (GSE10245) was downloaded from the National Center for Biotechnology Information Gene Expression Omnibus database, including 40 lung adenocarcinoma and 18 lung squamous cell carcinoma tissues. A total of 8 disease genes were identified using Naïve Bayesian Classifier based on the Maximum Relevance Minimum Redundancy feature selection method following preprocessing. An additional 21 candidate genes were selected using the shortest path analysis with Dijkstra's algorithm. The AURKA and SLC7A2 genes were selected three and two times in the shortest path analysis, respectively. All those genes participate in a number of important pathways, such as oocyte meiosis, cell cycle and cancer pathways with Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. The present findings may provide novel insights into the pathogenesis of NSCLC and enable the development of novel therapeutic strategies. However, further investigation is required to confirm these findings.

Radiomics to predict immunotherapy-induced pneumonitis: proof of concept.

We present the first reported work that explores the potential of radiomics to predict patients who are at risk for developing immunotherapy-induced pneumonitis. Despite promising results with immunotherapies, immune-related adverse events (irAEs) are challenging. Although less common, pneumonitis is a potentially fatal irAE. Thus, early detection is critical for improving treatment outcomes; an urgent need to identify biomarkers that predict patients at risk for pneumonitis exists. Radiomics, an emerging field, is the automated extraction of high fidelity, high-dimensional imaging features from standard medical images and allows for comprehensive visualization and characterization of the tissue of interest and corresponding microenvironment. In this pilot study, we sought to determine whether radiomics has the potential to predict development of pneumonitis. We performed radiomic analyses using baseline chest computed tomography images of patientswho did (N=2) and did not (N=30) develop immunotherapy-induced pneumonitis. We extracted 1860 radiomic features in each patient. Maximum relevance and minimum redundancy feature selection method, anomaly detection algorithm, and leave-one-out cross-validation identified radiomic features that were significantly different and predicted subsequent immunotherapy-induced pneumonitis (accuracy, 100% [p=0.0033]). This study suggests that radiomic features can classify and predict those patients at baseline who will subsequently develop immunotherapy-induced pneumonitis, further enabling risk-stratification that will ultimately lead to better treatment outcomes.

Mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification

This paper presents a hybrid filter–wrapper feature subset selection algorithm based on particle swarm optimization (PSO) for support vector machine (SVM) classification. The filter model is based on the mutual information and is a composite measure of feature relevance and redundancy with respect to the feature subset selected. The wrapper model is a modified discrete PSO algorithm. This hybrid algorithm, called maximum relevance minimum redundancy PSO ( mr 2 PSO), is novel in the sense that it uses the mutual information available from the filter model to weigh the bit selection probabilities in the discrete PSO. Hence, mr 2 PSO uniquely brings together the efficiency of filters and the greater accuracy of wrappers. The proposed algorithm is tested over several well-known benchmarking datasets. The performance of the proposed algorithm is also compared with a recent hybrid filter–wrapper algorithm based on a genetic algorithm and a wrapper algorithm based on PSO. The results show that the mr 2 PSO algorithm is competitive in terms of both classification accuracy and computational performance.