Feature Selection using FFS and PCA in Biomedical Data Classification with AdaBoost-SVM

Classification of biomedical data plays a significant role in prediction and diagnosis of disease. The existence of redundant and irrelevant features is one of the major problems in biomedical data classification. Excluding these features can improve the performance of classification algorithm. Feature selection is the problem of selecting a subset of features without reducing the accuracy of the original set of features. These algorithms are divided into three categories: wrapper, filter, and embedded methods. Wrapper methods use the learning algorithm for selection of features while filter methods use statistical characteristics of data. In the embedded methods, feature selection process combines with the learning process. Population-based metaheuristics can be applied for wrapper feature selection. In these algorithms, a population of candidate solutions is created. Then, they try to improve the objective function using some operators. This chapter presents the application of population-based feature selection to deal with issues of high dimensionality in the biomedical data classification. The result shows that population-based feature selection has presented acceptable performance in biomedical data classification.

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Feature selection algorithm for high dimensional biomedical data classification based on redundant removal

10.14236/ewic/hci2018.230 ◽

2018 ◽

Author(s):

Bingtao Zhang ◽

Peng Cao ◽

Yi Zhang ◽

Chaochao Zhang ◽

Zhe Li ◽

...

Keyword(s):

Feature Selection ◽

Data Classification ◽

High Dimensional ◽

Biomedical Data ◽

Selection Algorithm ◽

Feature Selection Algorithm

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Population-Based Feature Selection for Biomedical Data Classification

Biomedical Engineering ◽

10.4018/978-1-5225-3158-6.ch008 ◽

2018 ◽

pp. 199-231

Author(s):

Seyed Jalaleddin Mousavirad ◽

Hossein Ebrahimpour-Komleh

Keyword(s):

Feature Selection ◽

Learning Algorithm ◽

Selection Process ◽

Data Classification ◽

Population Based ◽

Statistical Characteristics ◽

Biomedical Data ◽

Filter Methods ◽

Embedded Methods

Classification of biomedical data plays a significant role in prediction and diagnosis of disease. The existence of redundant and irrelevant features is one of the major problems in biomedical data classification. Excluding these features can improve the performance of classification algorithm. Feature selection is the problem of selecting a subset of features without reducing the accuracy of the original set of features. These algorithms are divided into three categories: wrapper, filter, and embedded methods. Wrapper methods use the learning algorithm for selection of features while filter methods use statistical characteristics of data. In the embedded methods, feature selection process combines with the learning process. Population-based metaheuristics can be applied for wrapper feature selection. In these algorithms, a population of candidate solutions is created. Then, they try to improve the objective function using some operators. This chapter presents the application of population-based feature selection to deal with issues of high dimensionality in the biomedical data classification. The result shows that population-based feature selection has presented acceptable performance in biomedical data classification.

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A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification

Network Modeling Analysis in Health Informatics and Bioinformatics ◽

10.1007/s13721-021-00313-7 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Gayathri Nagarajan ◽

L. D. Dhinesh Babu

Keyword(s):

Feature Selection ◽

Search Algorithm ◽

Data Classification ◽

Rank Aggregation ◽

Selection Model ◽

Biomedical Data ◽

Model Based ◽

Fuzzy Techniques

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Feature Selection Algorithm for High-dimensional Biomedical Data Using Information Gain and Improved Chemical Reaction Optimization

Current Bioinformatics ◽

10.2174/1574893615666200204154358 ◽

2021 ◽

Vol 15 (8) ◽

pp. 912-926

Author(s):

Ge Zhang ◽

Pan Yu ◽

Jianlin Wang ◽

Chaokun Yan

Keyword(s):

Feature Selection ◽

Chemical Reaction ◽

Information Gain ◽

Feature Selection Method ◽

Search Space ◽

Neighborhood Search ◽

Biomedical Data ◽

Chemical Reaction Optimization ◽

Search Mechanism ◽

Reaction Optimization

Background: There have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. However, these datasets usually involve thousands of features and include much irrelevant or redundant information, which leads to confusion during diagnosis. Feature selection is a solution that consists of finding the optimal subset, which is known to be an NP problem because of the large search space. Objective: For the issue, this paper proposes a hybrid feature selection method based on an improved chemical reaction optimization algorithm (ICRO) and an information gain (IG) approach, which called IGICRO. Methods: IG is adopted to obtain some important features. The neighborhood search mechanism is combined with ICRO to increase the diversity of the population and improve the capacity of local search. Results: Experimental results of eight public available data sets demonstrate that our proposed approach outperforms original CRO and other state-of-the-art approaches.

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