scholarly journals B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

Computers ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 136
Author(s):  
Mohammad H. Nadimi-Shahraki ◽  
Mahdis Banaie-Dezfouli ◽  
Hoda Zamani ◽  
Shokooh Taghian ◽  
Seyedali Mirjalili
Keyword(s):  
Feature Selection ◽  
Medical Technology ◽  
Transfer Functions ◽  
Large Datasets ◽  
Experimental Results ◽  
Metaheuristic Algorithms ◽  
Superior Performance ◽  
Friedman Test ◽  
Feature Selection Problem ◽  
Metaheuristic Optimization Algorithms

Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select effective features. However, most of them are not effective and scalable enough to select effective features from large medical datasets as well as small ones. Therefore, in this paper, a binary moth-flame optimization (B-MFO) is proposed to select effective features from small and large medical datasets. Three categories of B-MFO were developed using S-shaped, V-shaped, and U-shaped transfer functions to convert the canonical MFO from continuous to binary. These categories of B-MFO were evaluated on seven medical datasets and the results were compared with four well-known binary metaheuristic optimization algorithms: BPSO, bGWO, BDA, and BSSA. In addition, the convergence behavior of the B-MFO and comparative algorithms were assessed, and the results were statistically analyzed using the Friedman test. The experimental results demonstrate a superior performance of B-MFO in solving the feature selection problem for different medical datasets compared to other comparative algorithms.

scholarly journals Binary Political Optimizer for Feature Selection Using Gene Expression Data

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Ghaith Manita ◽  
Ouajdi Korbaa
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Transfer Functions ◽  
Research Field ◽  
Sigmoid Function ◽  
Expression Data ◽  
Feature Selection Problem ◽  
Single Experiment ◽  
Comparative Results

DNA Microarray technology is an emergent field, which offers the possibility of obtaining simultaneous estimates of the expression levels of several thousand genes in an organism in a single experiment. One of the most significant challenges in this research field is to select high relevant genes from gene expression data. To address this problem, feature selection is a well-known technique to eliminate unnecessary genes in order to ensure accurate classification results. This paper proposes a binary version of Political Optimizer (PO) to solve feature selection problem using gene expression data. Two transfer functions are used to design a binary PO. The first one is based on Sigmoid function and will be noted as BPO-S, while the second one is based on V-shaped function and will be noted as BPO-V. The proposed methods are evaluated using 9 biological datasets and compared with 8 binary well-known metaheuristics. The comparative results show the prevalent performance of the BPO methods especially BPO-V in comparison with other techniques.

scholarly journals Methods for adapting the leaping frog algorithm to the binary search space when solving the feature selection problem

2020 ◽  
Vol 23 (4) ◽  
pp. 57-62
Author(s):  
M. B. Bardamova ◽  
◽  
A. G. Buymov ◽  
V. F. Tarasenko ◽  
◽  
...  
Keyword(s):  
Feature Selection ◽  
Search Space ◽  
Data Sets ◽  
Shuffled Frog Leaping Algorithm ◽  
Feature Selection Problem ◽  
Fuzzy Classifiers ◽  
Shuffled Frog Leaping ◽  
Transformation Functions ◽  
Selection For ◽  
Metaheuristic Optimization Algorithms

The feature selection is an important step in constructing any classifier. Binary versions of metaheuristic optimization algorithms are often used for selection. However, many metaheuristics are originally created to work in the continuous search space, so they need to be specially adapted to the binary space. In this paper, the authors propose fifteen ways to binarize the Shuffled frog leaping algorithm based on the following methods: modified algebraic operations, merge operation, and transformation functions. The efficiency of the binary algorithm was tested in the problem of feature selection for fuzzy classifiers on data sets from the KEEL repository. The results show that all the described methods of binarization allow reducing the features, while increasing the overall accuracy of classification.

Hybrid binary Sine Cosine Algorithm and Ant Lion Optimization (SCALO) approaches for feature selection problem

2020 ◽  
Vol 09 (01) ◽  
pp. 1950021
Author(s):  
Rahul Hans ◽  
Harjot Kaur
Keyword(s):  
Feature Selection ◽  
Optimization Problem ◽  
Transfer Functions ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Ant Lion Optimization ◽  
Ant Lion Optimizer ◽  
Sine Cosine Algorithm ◽  
Ant Lion ◽  
Better Than

These days, a massive quantity of data is produced online and is incorporated into a variety of datasets in the form of features, however there are lot of features in these datasets that may not be relevant to the problem. In this perspective, feature selection aids to improve the classification accuracy with lesser number of features, which can be well thought-out as an optimization problem. In this paper, Sine Cosine Algorithm (SCA) hybridized with Ant Lion Optimizer (ALO) to form a hybrid Sine Cosine Ant Lion Optimizer (SCALO) is proposed. The proposed algorithm is mapped to its binary versions by using the concept of transfer functions, with the objective to eliminate the inappropriate features and to enhance the accuracy of the classification algorithm (or in any case remains the same). For the purpose of experimentation, this research considers 18 diverse datasets and moreover, the performance of the binary versions of SCALO is compared with some of the latest metaheuristic algorithms, on the basis of various criterions. It can be observed that the binary versions of SCALO perform better than the other algorithms on various evaluation criterions for solving feature selection problem.

scholarly journals S-Shaped versus V-Shaped Transfer Functions for Binary Manta Ray Foraging Optimization in Feature Selection Problem

2020 ◽  
Author(s):  
Kushal Kanti Ghosh ◽  
Ritam Guha ◽  
Suman Kumar Bera ◽  
Neeraj Kumar ◽  
Ram Sarkar
Keyword(s):  
Feature Selection ◽  
Transfer Functions ◽  
Search Space ◽  
Optimization Techniques ◽  
Classification Model ◽  
Feature Selection Problem ◽  
The Core ◽  
Core Concepts ◽  
Manta Ray ◽  
State Of Art

Abstract Feature selection (FS) is considered as one of the core concepts in the areas of machine learning and data mining which immensely impacts the performance of classification model. Through FS, irrelevant or partially relevant features can be eliminated which in turn helps in enhancing the performance of the model. Over the years, researchers have applied different meta-heuristic optimization techniques for the purpose of FS as these overcome the limitations of traditional optimization approaches. Going by the trend, we introduce a new FS approach based on a recently proposed meta-heuristic algorithm called Manta Ray Foraging Optimization (MRFO) which is developed following the food foraging nature of the Manta rays, one of the largest known marine creatures. As MRFO is apposite for continuous search space problems, we have adapted a binary version of MRFO to t it into the problem of FS by applying eight different transfer functions belonging to two different families: S-shaped and V-shaped. We have evaluated the eight binary versions of MRFO on 18 standard UCI datasets. Of these, the best one is considered for comparison with 16 recently proposed meta-heuristic FS approaches. The results show that MRFO outperforms the state-of-art methods in terms of both classification accuracy and number of features selected.

scholarly journals Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarv Priya ◽  
Tanya Aggarwal ◽  
Caitlin Ward ◽  
Girish Bathla ◽  
Mathews Jacob ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pulmonary Hypertension ◽  
Feature Selection ◽  
Subgroup Analysis ◽  
Cardiac Mri ◽  
Intraclass Correlation ◽  
Poor Performance ◽  
Superior Performance ◽  
Feature Filtering ◽  
The Impact

AbstractSide experiments are performed on radiomics models to improve their reproducibility. We measure the impact of myocardial masks, radiomic side experiments and data augmentation for information transfer (DAFIT) approach to differentiate patients with and without pulmonary hypertension (PH) using cardiac MRI (CMRI) derived radiomics. Feature extraction was performed from the left ventricle (LV) and right ventricle (RV) myocardial masks using CMRI in 82 patients (42 PH and 40 controls). Various side study experiments were evaluated: Original data without and with intraclass correlation (ICC) feature-filtering and DAFIT approach (without and with ICC feature-filtering). Multiple machine learning and feature selection strategies were evaluated. Primary analysis included all PH patients with subgroup analysis including PH patients with preserved LVEF (≥ 50%). For both primary and subgroup analysis, DAFIT approach without feature-filtering was the highest performer (AUC 0.957–0.958). ICC approaches showed poor performance compared to DAFIT approach. The performance of combined LV and RV masks was superior to individual masks alone. There was variation in top performing models across all approaches (AUC 0.862–0.958). DAFIT approach with features from combined LV and RV masks provide superior performance with poor performance of feature filtering approaches. Model performance varies based upon the feature selection and model combination.

scholarly journals Chunking and cooperation in particle swarm optimization for feature selection

2021 ◽  
Author(s):  
Malek Sarhani ◽  
Stefan Voß
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Cooperative Learning ◽  
Optimization Problems ◽  
Practical Importance ◽  
Particle Swarm ◽  
Large Datasets ◽  
Social Phenomena ◽  
Swarm Optimization ◽  
Complex Optimization

AbstractBio-inspired optimization aims at adapting observed natural behavioral patterns and social phenomena towards efficiently solving complex optimization problems, and is nowadays gaining much attention. However, researchers recently highlighted an inconsistency between the need in the field and the actual trend. Indeed, while nowadays it is important to design innovative contributions, an actual trend in bio-inspired optimization is to re-iterate the existing knowledge in a different form. The aim of this paper is to fill this gap. More precisely, we start first by highlighting new examples for this problem by considering and describing the concepts of chunking and cooperative learning. Second, by considering particle swarm optimization (PSO), we present a novel bridge between these two notions adapted to the problem of feature selection. In the experiments, we investigate the practical importance of our approach while exploring both its strength and limitations. The results indicate that the approach is mainly suitable for large datasets, and that further research is needed to improve the computational efficiency of the approach and to ensure the independence of the sub-problems defined using chunking.

scholarly journals PCDRN: Progressive Cascade Deep Residual Network for Pansharpening

2020 ◽  
Vol 12 (4) ◽  
pp. 676 ◽  
Author(s):  
Yong Yang ◽  
Wei Tu ◽  
Shuying Huang ◽  
Hangyuan Lu
Keyword(s):  
High Resolution ◽  
Loss Function ◽  
High Frequency ◽  
Experimental Results ◽  
Superior Performance ◽  
Residual Network ◽  
High Quality ◽  
Convolution Approach ◽  
Visual Assessments ◽  
Coarse To Fine

Pansharpening is the process of fusing a low-resolution multispectral (LRMS) image with a high-resolution panchromatic (PAN) image. In the process of pansharpening, the LRMS image is often directly upsampled by a scale of 4, which may result in the loss of high-frequency details in the fused high-resolution multispectral (HRMS) image. To solve this problem, we put forward a novel progressive cascade deep residual network (PCDRN) with two residual subnetworks for pansharpening. The network adjusts the size of an MS image to the size of a PAN image twice and gradually fuses the LRMS image with the PAN image in a coarse-to-fine manner. To prevent an overly-smooth phenomenon and achieve high-quality fusion results, a multitask loss function is defined to train our network. Furthermore, to eliminate checkerboard artifacts in the fusion results, we employ a resize-convolution approach instead of transposed convolution for upsampling LRMS images. Experimental results on the Pléiades and WorldView-3 datasets prove that PCDRN exhibits superior performance compared to other popular pansharpening methods in terms of quantitative and visual assessments.

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