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Feature Selection for Optimized High-dimensional Biomedical Data using the Improved Shuffled Frog Leaping Algorithm

Bin Hu, Yongqiang Dai, Yun Su, Philip Moore, Xiaowei Zhang, Chengsheng Mao, Jing Chen, Lixin Xu
2018 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
High dimensional biomedical datasets contain thousands of features which can be used in molecular diagnosis of disease, however, such datasets contain many irrelevant or weak correlation features which  ...  Our proposed approach explores the space of possible subsets to obtain the set of features that maximizes the predictive accuracy and minimizes irrelevant features in high-dimensional biomedical data.  ...  Early diagnosis plays an important role for clinicians and patients in the control and management of disease [1] and high dimensional biomedical data sets have been used in diagnosis.  ... 
doi:10.1109/tcbb.2016.2602263 pmid:28113635 fatcat:zv42f77rtfao7hctyxalo7ov3y

Feature Selection in High Dimensional Biomedical Data Based on BF-SFLA

Yongqiang Dai, Lili Niu, Linjing Wei, Jie Tang
2022 Frontiers in Neuroscience  
High-dimensional biomedical data contained many irrelevant or weakly correlated features, which affected the efficiency of disease diagnosis.  ...  This manuscript presented a feature selection method for high-dimensional biomedical data based on the chemotaxis foraging-shuffled frog leaping algorithm (BF-SFLA).  ...  LN normalized the data. LW and JT made grammatical modifications to the manuscript. All authors contributed to the article and approved the submitted version.  ... 
doi:10.3389/fnins.2022.854685 pmid:35509450 pmcid:PMC9058075 fatcat:2ceguoefcngh3ddiog72lole7q

Feature Selection Using Improved Teaching Learning Based Algorithm on Chronic Kidney Disease Dataset

Manonmani. M, Sarojini Balakrishnan
2020 Procedia Computer Science  
Innumerable feature selection methods have been presented in state-of-arts literature to tackle the problems of high dimensional data.  ...  Innumerable feature selection methods have been presented in state-of-arts literature to tackle the problems of high dimensional data.  ...  High dimensional medical data will result in reduced efficiency of the computational models [2] .  ... 
doi:10.1016/j.procs.2020.04.178 fatcat:7c4oouylbzbtpaifji2k6v3t5u

Swarm Intelligence Algorithms for Feature Selection: A Review

Lucija Brezočnik, Iztok Fister, Vili Podgorelec
2018 Applied Sciences  
Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets.  ...  To be able to learn from data, the dimensionality of the data should be reduced first.  ...  Some studies [45, 97] use the improved SFLA algorithm on high-dimensional biomedical data for disease diagnosis. Image based FS is addressed in [99] .  ... 
doi:10.3390/app8091521 fatcat:oymef7dijzekhin4leripkmjje

Multiple Lesions Detection of Fundus Images based on Convolution Neural Network Algorithm with Improved SFLA

Weiping Ding, Ying Sun, Longjie Ren, Hengrong Ju, Zhihao Feng, Ming Li
2020 IEEE Access  
The experiment of the detection of fundus image lesions shows that the accuracy rate of SFCNN is better improved in both single lesion detection and overall detection, compared with other algorithms.  ...  The algorithm uses the efficient search ability of the shuffled frog leaping algorithm to optimize the weight initialization and back propagation of the convolutional neural network.  ...  However, the running time of SFLA-CNN with the same number of runs is generally longer than that of SFCNN. So the improved SFLA is more suitable for the improvement of this paper.  ... 
doi:10.1109/access.2020.2996569 fatcat:2ibss32hgbha7hotlefjkoftza

Effective hybrid feature selection using different bootstrap enhances cancers classification performance

Noura Mohammed Abdelwahed, Gh. S. El-Tawel, M. A. Makhlouf
2022 BioData Mining  
Random forest for selection (RFS) proves its effectiveness in selecting the effective features and improving the over-fitting problem.  ...  Background Machine learning can be used to predict the different onset of human cancers. Highly dimensional data have enormous, complicated problems.  ...  Mohamed for his help and support, thanks to Dr. Ghada for her support and guidance.  ... 
doi:10.1186/s13040-022-00304-y fatcat:i46erwiuxjcllkduqnmwr3uvwy

Brain Tumor Detection Using Image Processing

Bhushan, Pawar, Siddhi Ganbote, Snehal Shitole, Mansi Sarode
2016 International Engineering Research Journal   unpublished
ARTICLE INFO Tumor is the one of the most common brain diesease and this is the reason for the diagnosis & treatment of the brain tumor has vital importance.  ...  In this paper we are going to discuss the methods for detection of brain tumor and evaluate them.  ...  Anupurba Nandi [9] proposed a method for improving the classification of brain tumor by using Clustering and morphological operators used for biomedical image segmentation as it is used in unsupervised  ... 
fatcat:alcibiphnfgmxgyfxutbxze5be

Optimizing Problem of Brain Tumor Detection Using Image Processing

Bhushan Pawar1, Siddhi Ganbote2, Snehal Shitole3, Mansi Sarode4, Rupali Pandharpatte5
International Research Journal of Engineering and Technology (IRJET)   unpublished
Tumor is the one of the most common brain disease and this is the reason for the diagnosis & treatment of the brain tumor has vital importance.  ...  In this paper we are going to discuss the methods for detection of brain tumor and evaluate them.  ...  Anupurba Nandi [9] proposed a method for improving the classification of brain tumor by using Clustering and morphological operators used for biomedical image segmentation as it is used in unsupervised  ... 
fatcat:brw73fawoveztji4yljsz4xi3e