scholarly journals Comparison of Methods for Feature Selection in Clustering of High-Dimensional RNA-Sequencing Data to Identify Cancer Subtypes

2021 ◽  
Vol 12 ◽  
Author(s):  
David Källberg ◽  
Linda Vidman ◽  
Patrik Rydén
Keyword(s):  
Feature Selection ◽  
Rna Sequencing ◽  
Feature Selection Method ◽  
High Dimensional ◽  
Adjusted Rand Index ◽  
Data Sets ◽  
Selection Methods ◽  
Sequencing Data ◽  
Cancer Data ◽  
Cancer Subtype

Cancer subtype identification is important to facilitate cancer diagnosis and select effective treatments. Clustering of cancer patients based on high-dimensional RNA-sequencing data can be used to detect novel subtypes, but only a subset of the features (e.g., genes) contains information related to the cancer subtype. Therefore, it is reasonable to assume that the clustering should be based on a set of carefully selected features rather than all features. Several feature selection methods have been proposed, but how and when to use these methods are still poorly understood. Thirteen feature selection methods were evaluated on four human cancer data sets, all with known subtypes (gold standards), which were only used for evaluation. The methods were characterized by considering mean expression and standard deviation (SD) of the selected genes, the overlap with other methods and their clustering performance, obtained comparing the clustering result with the gold standard using the adjusted Rand index (ARI). The results were compared to a supervised approach as a positive control and two negative controls in which either a random selection of genes or all genes were included. For all data sets, the best feature selection approach outperformed the negative control and for two data sets the gain was substantial with ARI increasing from (−0.01, 0.39) to (0.66, 0.72), respectively. No feature selection method completely outperformed the others but using the dip-rest statistic to select 1000 genes was overall a good choice. The commonly used approach, where genes with the highest SDs are selected, did not perform well in our study.

scholarly journals Robust Feature Selection on Incomplete Data

2018 ◽  
Author(s):  
Wei Zheng ◽  
Xiaofeng Zhu ◽  
Yonghua Zhu ◽  
Shichao Zhang
Keyword(s):  
Feature Selection ◽  
Incomplete Data ◽  
High Dimensional ◽  
Data Sets ◽  
Selection Methods ◽  
Limited Ability ◽  
Training Samples ◽  
Indicator Matrix ◽  
Selection Framework ◽  
Incomplete Datasets

Feature selection is an indispensable preprocessing procedure for high-dimensional data analysis,but previous feature selection methods usually ignore sample diversity (i.e., every sample has individual contribution for the model construction) andhave limited ability to deal with incomplete datasets where a part of training samples have unobserved data. To address these issues, in this paper, we firstly propose a robust feature selectionframework to relieve the influence of outliers, andthen introduce an indicator matrix to avoid unobserved data to take participation in numerical computation of feature selection so that both our proposed feature selection framework and exiting feature selection frameworks are available to conductfeature selection on incomplete data sets. We further propose a new optimization algorithm to optimize the resulting objective function as well asprove our algorithm to converge fast. Experimental results on both real and artificial incompletedata sets demonstrated that our proposed methodoutperformed the feature selection methods undercomparison in terms of clustering performance.  

SPARSITY SCORE: A NOVEL GRAPH-PRESERVING FEATURE SELECTION METHOD

2014 ◽  
Vol 28 (04) ◽  
pp. 1450009 ◽  
Author(s):  
MINGXIA LIU ◽  
DAOQIANG ZHANG
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Original Data ◽  
Selection Method ◽  
Compact Representation ◽  
Data Sets ◽  
Selection Methods ◽  
Clustering And Classification ◽  
Filter Type ◽  
Selection Framework

As thousands of features are available in many pattern recognition and machine learning applications, feature selection remains an important task to find the most compact representation of the original data. In the literature, although a number of feature selection methods have been developed, most of them focus on optimizing specific objective functions. In this paper, we first propose a general graph-preserving feature selection framework where graphs to be preserved vary in specific definitions, and show that a number of existing filter-type feature selection algorithms can be unified within this framework. Then, based on the proposed framework, a new filter-type feature selection method called sparsity score (SS) is proposed. This method aims to preserve the structure of a pre-defined l1 graph that is proven robust to data noise. Here, the modified sparse representation based on an l1-norm minimization problem is used to determine the graph adjacency structure and corresponding affinity weight matrix simultaneously. Furthermore, a variant of SS called supervised SS (SuSS) is also proposed, where the l1 graph to be preserved is constructed by using only data points from the same class. Experimental results of clustering and classification tasks on a series of benchmark data sets show that the proposed methods can achieve better performance than conventional filter-type feature selection methods.

scholarly journals Multi-Label Feature Selection Method Based on Dynamic Weight

2021 ◽  
Author(s):  
Ping Zhang ◽  
Jiyao Sheng ◽  
Wanfu Gao ◽  
Juncheng Hu ◽  
Yonghao Li
Keyword(s):  
Feature Selection ◽  
Dynamic Change ◽  
Feature Selection Method ◽  
Selection Method ◽  
Data Sets ◽  
Selection Methods ◽  
Real World Data ◽  
Amount Of Information ◽  
The Difference ◽  
Classification Information

Abstract Multi-label feature selection attracts considerable attention from multi-label learning. Information-theory based multi-label feature selection methods intend to select the most informative features and reduce the uncertain amount of information of labels. Previous methods regard the uncertain amount of information of labels as constant. In fact, as the classification information of the label set is captured by features, the remaining uncertainty of each label is changing dynamically. In this paper, we categorize labels into two groups: one contains the labels with few remaining uncertainty, which means that most of classification information with respect to the labels has been obtained by the already-selected features; another group contains the labels with extensive remaining uncertainty, which means that the classification information of these labels is neglected by already-selected features. Feature selection aims to select the new features with highly relevant to the labels in the second group. Existing methods do not distinguish the difference between two label groups and ignore the dynamic change amount of information of labels. To this end, a Relevancy Ratio is designed to clarify the dynamic change amount of information of each label under the condition of the already-selected features. Afterwards, a Weighted Feature Relevancy is defined to evaluate the candidate features. Finally, a new multi-label Feature Selection method based on Weighted Feature Relevancy (WFRFS) is proposed. The experiments obtain encouraging results of WFRFS in comparison to six multi-label feature selection methods on thirteen real-world data sets.

scholarly journals Feature selection to improve generalization of genetic programming for high-dimensional symbolic regression

2021 ◽  
Author(s):  
Qi Chen ◽  
Mengjie Zhang ◽  
Bing Xue
Keyword(s):  
Feature Selection ◽  
Genetic Programming ◽  
Feature Selection Method ◽  
Selection Method ◽  
Symbolic Regression ◽  
Superior Performance ◽  
High Dimensional ◽  
Selection Methods ◽  
Personal Use ◽  
New Feature

When learning from high-dimensional data for symbolic regression (SR), genetic programming (GP) typically could not generalize well. Feature selection, as a data preprocessing method, can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalization ability. However, in GP for high-dimensional SR, feature selection before learning is seldom considered. In this paper, we propose a new feature selection method based on permutation to select features for high-dimensional SR using GP. A set of experiments has been conducted to investigate the performance of the proposed method on the generalization of GP for high-dimensional SR. The regression results confirm the superior performance of the proposed method over the other examined feature selection methods. Further analysis indicates that the models evolved by the proposed method are more likely to contain only the truly relevant features and have better interpretability. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

A causality-inspired feature selection method for cancer imbalanced high-dimensional data

2021 ◽  
Author(s):  
Yijun Liu ◽  
Qiang Huang ◽  
Huiyan Sun ◽  
Yi Chang
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Class Imbalance ◽  
Selection Method ◽  
Learning Performance ◽  
High Dimensional ◽  
Final Decision ◽  
Selection Methods ◽  
Cancer Initiation ◽  
Cancer Types

It is significant but challenging to explore a subset of robust biomarkers to distinguish cancer from normal samples on high-dimensional imbalanced cancer biological omics data. Although many feature selection methods addressing high dimensionality and class imbalance have been proposed, they rarely pay attention to the fact that most classes will dominate the final decision-making when the dataset is imbalanced, leading to instability when it expands downstream tasks. Because of causality invariance, causal relationship inference is considered an effective way to improve machine learning performance and stability. This paper proposes a Causality-inspired Least Angle Nonlinear Distributed (CLAND) feature selection method, consisting of two branches with a class-wised branch and a sample-wised branch representing two deconfounder strategies, respectively. We compared the performance of CLAND with other advanced feature selection methods in transcriptional data of six cancer types with different imbalance ratios. The genes selected by CLAND have superior accuracy, stability, and generalization in the downstream classification tasks, indicating potential causality for identifying cancer samples. Furthermore, these genes have also been demonstrated to play an essential role in cancer initiation and progression through reviewing the literature.

scholarly journals Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 4182-4189
Author(s):  
Qiang Huang ◽  
Tingyu Xia ◽  
Huiyan Sun ◽  
Makoto Yamada ◽  
Yi Chang
Keyword(s):  
Feature Selection ◽  
Rapid Development ◽  
Feature Selection Method ◽  
High Dimensional ◽  
Selection Methods ◽  
Feature Selection Problem ◽  
Unsupervised Feature Selection ◽  
Signed Networks ◽  
Signed Network ◽  
Nonlinear Feature

With the rapid development of social media services in recent years, relational data are explosively growing. The signed network, which consists of a mixture of positive and negative links, is an effective way to represent the friendly and hostile relations among nodes, which can represent users or items. Because the features associated with a node of a signed network are usually incomplete, noisy, unlabeled, and high-dimensional, feature selection is an important procedure to eliminate irrelevant features. However, existing network-based feature selection methods are linear methods, which means they can only select features that having the linear dependency on the output values. Moreover, in many social data, most nodes are unlabeled; therefore, selecting features in an unsupervised manner is generally preferred. To this end, in this paper, we propose a nonlinear unsupervised feature selection method for signed networks, called SignedLasso. This method can select a small number of important features with nonlinear associations between inputs and output from a high-dimensional data. More specifically, we formulate unsupervised feature selection as a nonlinear feature selection problem with the Hilbert-Schmidt Independence Criterion Lasso (HSIC Lasso), which can find a small number of features in a nonlinear manner. Then, we propose the use of a deep learning-based node embedding to represent node similarity without label information and incorporate the node embedding into the HSIC Lasso. Through experiments on two real world datasets, we show that the proposed algorithm is superior to existing linear unsupervised feature selection methods.

scholarly journals Feature selection to improve generalization of genetic programming for high-dimensional symbolic regression

2021 ◽  
Author(s):  
Qi Chen ◽  
Mengjie Zhang ◽  
Bing Xue
Keyword(s):  
Feature Selection ◽  
Genetic Programming ◽  
Feature Selection Method ◽  
Selection Method ◽  
Symbolic Regression ◽  
Superior Performance ◽  
High Dimensional ◽  
Selection Methods ◽  
Personal Use ◽  
New Feature

When learning from high-dimensional data for symbolic regression (SR), genetic programming (GP) typically could not generalize well. Feature selection, as a data preprocessing method, can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalization ability. However, in GP for high-dimensional SR, feature selection before learning is seldom considered. In this paper, we propose a new feature selection method based on permutation to select features for high-dimensional SR using GP. A set of experiments has been conducted to investigate the performance of the proposed method on the generalization of GP for high-dimensional SR. The regression results confirm the superior performance of the proposed method over the other examined feature selection methods. Further analysis indicates that the models evolved by the proposed method are more likely to contain only the truly relevant features and have better interpretability. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

scholarly journals Feature Selection and Feature Stability Measurement Method for High-Dimensional Small Sample Data Based on Big Data Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chengyuan Huang
Keyword(s):  
Feature Selection ◽  
Measurement Method ◽  
Feature Selection Method ◽  
Selection Method ◽  
Small Sample ◽  
High Dimensional ◽  
Selection Methods ◽  
Sample Data ◽  
Stability Measurement ◽  
The Stability

With the rapid development of artificial intelligence in recent years, the research on image processing, text mining, and genome informatics has gradually deepened, and the mining of large-scale databases has begun to receive more and more attention. The objects of data mining have also become more complex, and the data dimensions of mining objects have become higher and higher. Compared with the ultra-high data dimensions, the number of samples available for analysis is too small, resulting in the production of high-dimensional small sample data. High-dimensional small sample data will bring serious dimensional disasters to the mining process. Through feature selection, redundancy and noise features in high-dimensional small sample data can be effectively eliminated, avoiding dimensional disasters and improving the actual efficiency of mining algorithms. However, the existing feature selection methods emphasize the classification or clustering performance of the feature selection results and ignore the stability of the feature selection results, which will lead to unstable feature selection results, and it is difficult to obtain real and understandable features. Based on the traditional feature selection method, this paper proposes an ensemble feature selection method, Random Bits Forest Recursive Clustering Eliminate (RBF-RCE) feature selection method, combined with multiple sets of basic classifiers to carry out parallel learning and screen out the best feature classification results, optimizes the classification performance of traditional feature selection methods, and can also improve the stability of feature selection. Then, this paper analyzes the reasons for the instability of feature selection and introduces a feature selection stability measurement method, the Intersection Measurement (IM), to evaluate whether the feature selection process is stable. The effectiveness of the proposed method is verified by experiments on several groups of high-dimensional small sample data sets.

scholarly journals Evaluation of Feature Selection Methods on Psychosocial Education Data Using Additive Ratio Assessment

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 114
Author(s):  
Fitriani Muttakin ◽  
Jui-Tang Wang ◽  
Mulyanto Mulyanto ◽  
Jenq-Shiou Leu
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Performance Metrics ◽  
Prediction Models ◽  
Feature Selection Method ◽  
Selection Method ◽  
Research Trend ◽  
High Dimensional ◽  
Selection Methods ◽  
Education Data

Artificial intelligence, particularly machine learning, is the fastest-growing research trend in educational fields. Machine learning shows an impressive performance in many prediction models, including psychosocial education. The capability of machine learning to discover hidden patterns in large datasets encourages researchers to invent data with high-dimensional features. In contrast, not all features are needed by machine learning, and in many cases, high-dimensional features decrease the performance of machine learning. The feature selection method is one of the appropriate approaches to reducing the features to ensure machine learning works efficiently. Various selection methods have been proposed, but research to determine the essential subset feature in psychosocial education has not been established thus far. This research investigated and proposed methods to determine the best feature selection method in the domain of psychosocial education. We used a multi-criteria decision system (MCDM) approach with Additive Ratio Assessment (ARAS) to rank seven feature selection methods. The proposed model evaluated the best feature selection method using nine criteria from the performance metrics provided by machine learning. The experimental results showed that the ARAS is promising for evaluating and recommending the best feature selection method for psychosocial education data using the teacher’s psychosocial risk levels dataset.

Feature Selection by Applying Parallel Collaborative Evolutionary GA

2012 ◽  
Vol 263-266 ◽  
pp. 2074-2081
Author(s):  
Zhi Cheng Qu ◽  
Qin Yang ◽  
Bin Jiang
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Information Gain ◽  
Feature Selection Method ◽  
Data Sets ◽  
Selection Methods ◽  
Text Data ◽  
Evolutionary Genetic ◽  
Parallel Strategy ◽  
Better Than

Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this paper, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that: For macro-average and micro-average , the presented method is better than three classical methods: Information Gain、x2 Statistics、 Mutual Information. For the consumed time, the presented method with a CPU is inferior to the above mentioned three methods, but the presented method is superior after using the parallel strategy.

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