scholarly journals Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms

Genes ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1814
Author(s):  
Yuanyuan Han ◽  
Lan Huang ◽  
Fengfeng Zhou
Keyword(s):  
Feature Selection ◽  
Swarm Intelligence ◽  
Solution Space ◽  
Recursive Feature Elimination ◽  
Feature Subset ◽  
Large P Small N ◽  
Hidden Patterns ◽  
The Individual ◽  
Target Searching ◽  
Selection Algorithms

Biological omics data such as transcriptomes and methylomes have the inherent “large p small n” paradigm, i.e., the number of features is much larger than that of the samples. A feature selection (FS) algorithm selects a subset of the transcriptomic or methylomic biomarkers in order to build a better prediction model. The hidden patterns in the FS solution space make it challenging to achieve a feature subset with satisfying prediction performances. Swarm intelligence (SI) algorithms mimic the target searching behaviors of various animals and have demonstrated promising capabilities in selecting features with good machine learning performances. Our study revealed that different SI-based feature selection algorithms contributed complementary searching capabilities in the FS solution space, and their collaboration generated a better feature subset than the individual SI feature selection algorithms. Nine SI-based feature selection algorithms were integrated to vote for the selected features, which were further refined by the dynamic recursive feature elimination framework. In most cases, the proposed Zoo algorithm outperformed the existing feature selection algorithms on transcriptomics and methylomics datasets.

Optimum Feature Subset for Optimizing Crop Yield Prediction Using Filter and Wrapper Approaches

2019 ◽  
Vol 35 (1) ◽  
pp. 9-14 ◽  
Author(s):  
P. S. Maya Gopal ◽  
R Bhargavi
Keyword(s):  
Feature Selection ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Crop Yield ◽  
Computational Time ◽  
Feature Subset ◽  
Yield Prediction ◽  
Selection Algorithm ◽  
Cultivable Land ◽  
Selection Algorithms

Abstract. In agriculture, crop yield prediction is critical. Crop yield depends on various features which can be categorized as geographical, climatic, and biological. Geographical features consist of cultivable land in hectares, canal length to cover the cultivable land, number of tanks and tube wells available for irrigation. Climatic features consist of rainfall, temperature, and radiation. Biological features consist of seeds, minerals, and nutrients. In total, 15 features were considered for this study to understand features impact on paddy crop yield for all seasons of each year. For selecting vital features, five filter and wrapper approaches were applied. For predicting accuracy of features selection algorithm, Multiple Linear Regression (MLR) model was used. The RMSE, MAE, R, and RRMSE metrics were used to evaluate the performance of feature selection algorithms. Data used for the analysis was drawn from secondary sources of state Agriculture Department, Government of Tamil Nadu, India, for over 30 years. Seventy-five percent of data was used for training and 25% was used for testing. Low computational time was also considered for the selection of best feature subset. Outcome of all feature selection algorithms have given similar results in the RMSE, RRMSE, R, and MAE values. The adjusted R2 value was used to find the optimum feature subset despite all the deviations. The evaluation of the dataset used in this work shows that total area of cultivation, number of tanks and open wells used for irrigation, length of canals used for irrigation, and average maximum temperature during the season of the crop are the best features for better crop yield prediction on the study area. The MLR gives 85% of model accuracy for the selected features with low computational time. Keywords: Feature selection algorithm, Model validation, Multiple linear regression, Performance metrics.

scholarly journals A Holistic Performance Comparison for Lung Cancer Classification Using Swarm Intelligence Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Sunil Kumar Prabhakar ◽  
Harikumar Rajaguru ◽  
Dong-Ok Won
Keyword(s):  
Lung Cancer ◽  
Feature Selection ◽  
Decision Trees ◽  
Swarm Intelligence ◽  
Microarray Data ◽  
Optimization Techniques ◽  
Feature Subset ◽  
F Test ◽  
New Feature

In the field of bioinformatics, feature selection in classification of cancer is a primary area of research and utilized to select the most informative genes from thousands of genes in the microarray. Microarray data is generally noisy, is highly redundant, and has an extremely asymmetric dimensionality, as the majority of the genes present here are believed to be uninformative. The paper adopts a methodology of classification of high dimensional lung cancer microarray data utilizing feature selection and optimization techniques. The methodology is divided into two stages; firstly, the ranking of each gene is done based on the standard gene selection techniques like Information Gain, Relief–F test, Chi-square statistic, and T-statistic test. As a result, the gathering of top scored genes is assimilated, and a new feature subset is obtained. In the second stage, the new feature subset is further optimized by using swarm intelligence techniques like Grasshopper Optimization (GO), Moth Flame Optimization (MFO), Bacterial Foraging Optimization (BFO), Krill Herd Optimization (KHO), and Artificial Fish Swarm Optimization (AFSO), and finally, an optimized subset is utilized. The selected genes are used for classification, and the classifiers used here are Naïve Bayesian Classifier (NBC), Decision Trees (DT), Support Vector Machines (SVM), and K-Nearest Neighbour (KNN). The best results are shown when Relief-F test is computed with AFSO and classified with Decision Trees classifier for hundred genes, and the highest classification accuracy of 99.10% is obtained.

MinE-RFE: determine the optimal subset from RFE by minimizing the subset-accuracy–defined energy

2019 ◽  
Vol 21 (2) ◽  
pp. 687-698 ◽  
Author(s):  
Ran Su ◽  
Xinyi Liu ◽  
Leyi Wei
Keyword(s):  
Feature Selection ◽  
Gaussian Mixture Models ◽  
Web Server ◽  
Gaussian Mixture ◽  
Recursive Feature Elimination ◽  
Optimal Subset ◽  
Spline Fitting ◽  
Comparative Results ◽  
Feature Selection Approach ◽  
Selection Algorithms

Abstract Recursive feature elimination (RFE), as one of the most popular feature selection algorithms, has been extensively applied to bioinformatics. During the training, a group of candidate subsets are generated by iteratively eliminating the least important features from the original features. However, how to determine the optimal subset from them still remains ambiguous. Among most current studies, either overall accuracy or subset size (SS) is used to select the most predictive features. Using which one or both and how they affect the prediction performance are still open questions. In this study, we proposed MinE-RFE, a novel RFE-based feature selection approach by sufficiently considering the effect of both factors. Subset decision problem was reflected into subset-accuracy space and became an energy-minimization problem. We also provided a mathematical description of the relationship between the overall accuracy and SS using Gaussian Mixture Models together with spline fitting. Besides, we comprehensively reviewed a variety of state-of-the-art applications in bioinformatics using RFE. We compared their approaches of deciding the final subset from all the candidate subsets with MinE-RFE on diverse bioinformatics data sets. Additionally, we also compared MinE-RFE with some well-used feature selection algorithms. The comparative results demonstrate that the proposed approach exhibits the best performance among all the approaches. To facilitate the use of MinE-RFE, we further established a user-friendly web server with the implementation of the proposed approach, which is accessible at http://qgking.wicp.net/MinE/. We expect this web server will be a useful tool for research community.

scholarly journals Pulsars detection by machine learning with very few features

2020 ◽  
Vol 493 (2) ◽  
pp. 1842-1854
Author(s):  
Haitao Lin ◽  
Xiangru Li ◽  
Ziying Luo
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
False Positive Rate ◽  
Recall Rate ◽  
Detection Performance ◽  
Recursive Feature Elimination ◽  
High Time Resolution ◽  
Detection Model ◽  
Positive Rate ◽  
Selection Algorithms

ABSTRACT It is an active topic to investigate the schemes based on machine learning (ML) methods for detecting pulsars as the data volume growing exponentially in modern surveys. To improve the detection performance, input features into an ML model should be investigated specifically. In the existing pulsar detection researches based on ML methods, there are mainly two kinds of feature designs: the empirical features and statistical features. Due to the combinational effects from multiple features, however, there exist some redundancies and even irrelevant components in the available features, which can reduce the accuracy of a pulsar detection model. Therefore, it is essential to select a subset of relevant features from a set of available candidate features and known as feature selection. In this work, two feature selection algorithms –Grid Search (GS) and Recursive Feature Elimination (RFE) – are proposed to improve the detection performance by removing the redundant and irrelevant features. The algorithms were evaluated on the Southern High Time Resolution University survey (HTRU-S) with five pulsar detection models. The experimental results verify the effectiveness and efficiency of our proposed feature selection algorithms. By the GS, a model with only two features reach a recall rate as high as 99 per cent and a false positive rate (FPR) as low as 0.65 per cent; by the RFE, another model with only three features achieves a recall rate of 99 per cent and an FPR of 0.16 per cent in pulsar candidates classification. Furthermore, this work investigated the number of features required as well as the misclassified pulsars by our models.

scholarly journals Constraint Programming Based Biomarker Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Manli Zhou ◽  
Youxi Luo ◽  
Guoquan Sun ◽  
Guoqin Mai ◽  
Fengfeng Zhou
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Programming Model ◽  
Feature Subset ◽  
Biomedical Knowledge ◽  
User Input ◽  
Optimization Function ◽  
Wet Lab ◽  
Constrained Programming ◽  
Selection Algorithms

Efficient and intuitive characterization of biological big data is becoming a major challenge for modern bio-OMIC based scientists. Interactive visualization and exploration of big data is proven to be one of the successful solutions. Most of the existing feature selection algorithms do not allow the interactive inputs from users in the optimizing process of feature selection. This study investigates this question as fixing a few user-input features in the finally selected feature subset and formulates these user-input features as constraints for a programming model. The proposed algorithm, fsCoP (feature selection based on constrained programming), performs well similar to or much better than the existing feature selection algorithms, even with the constraints from both literature and the existing algorithms. An fsCoP biomarker may be intriguing for further wet lab validation, since it satisfies both the classification optimization function and the biomedical knowledge. fsCoP may also be used for the interactive exploration of bio-OMIC big data by interactively adding user-defined constraints for modeling.

Feature selection for incomplete set-valued data

2021 ◽  
pp. 1-19
Author(s):  
Lulu Li
Keyword(s):  
Information System ◽  
Feature Selection ◽  
Practical Significance ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Uncertainty Measurement ◽  
Selection For ◽  
Average Size ◽  
Selection Algorithms ◽  
Information Values

Set-valued data is a significant kind of data, such as data obtained from different search engines, market data, patients’ symptoms and behaviours. An information system (IS) based on incomplete set-valued data is called an incomplete set-valued information system (ISVIS), which generalized model of a single-valued incomplete information system. This paper gives feature selection for an ISVIS by means of uncertainty measurement. Firstly, the similarity degree between two information values on a given feature of an ISVIS is proposed. Then, the tolerance relation on the object set with respect to a given feature subset in an ISVIS is obtained. Next, λ-reduction in an ISVIS is presented. What’s more, connections between the proposed feature selection and uncertainty measurement are exhibited. Lastly, feature selection algorithms based on λ-discernibility matrix, λ-information granulation, λ-information entropy and λ-significance in an ISVIS are provided. In order to better prove the practical significance of the provided algorithms, a numerical experiment is carried out, and experiment results show the number of features and average size of features by each feature selection algorithm.

Feature Selection Method Based on Parallel Binary Immune Quantum-Behaved Particle Swarm Optimization

2012 ◽  
Vol 546-547 ◽  
pp. 1538-1543 ◽  
Author(s):  
Chao Chen ◽  
Hao Dong Zhu
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Data Sets ◽  
Feature Subset ◽  
Swarm Optimization ◽  
Memory Space ◽  
Selection Algorithms

In order to enhance the operating speed and reduce the occupied memory space and filter out irrelevant or lower degree of features, feature selection algorithms must be used. However, most of existing feature selection methods are serial and are inefficient timely to be applied to massive text data sets, so it is a hotspot how to improve efficiency of feature selection by means of parallel thinking. This paper presented a feature selection method based on Parallel Binary Immune Quantum-Behaved Particle Swarm Optimization (PBIQPSO). The presented method uses the Binary Immune Quantum-Behaved Particle Swarm Optimization to select feature subset, takes advantage of multiple computing nodes to enhance time efficiency, so can acquire quickly the feature subsets which are more representative. Experimental results show that the method is effective.

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