Reducing bioinformatics data dimension with ABC-kNN | Semantic Scholar (2024)


Dimension Reduction (opens in a new tab)Bioinformatics (opens in a new tab)Fitness Evaluations (opens in a new tab)Heuristic Methods (opens in a new tab)Classification Problems (opens in a new tab)Execution Time (opens in a new tab)Swarm Intelligence (opens in a new tab)Computational Resources (opens in a new tab)Artificial Bee Colony (opens in a new tab)Gene Expression Analysis (opens in a new tab)

41 Citations

Feature selection methods in microarray gene expression data: a systematic mapping study
    Mahnaz VahmiyanM. KheirabadiEbrahim Akbari

    Computer Science, Biology

    Neural Computing and Applications

  • 2022

A systematic mapping study to analyze and synthesize the studies conducted on the FS techniques in microarrays and showed that classification is the most important task in FS.

  • 2
Mutational Slime Mould Algorithm for Gene Selection
    Feng QiuP. Zheng Haiping Lin

    Computer Science, Biology


  • 2022

Experimental results show that the continuous version of the algorithm achieves an optimal balance between local exploitation and global search capabilities, and the discrete version ofThe algorithm has the highest accuracy when selecting the least number of genes.

Artificial Bee Colony based Feature Selection for Effective Cardiovascular Disease Diagnosis
    R. Rajalaxmi

    Medicine, Computer Science

  • 2014

An effective algorithm that can remove irrelevant dimensions from large data and to predict more accurately the presence of disease is designed and results indicate that, BABC–Naive Bayesian outperform the other methods.

  • 14
Feature Reduction for Anomaly Detection in Manufacturing with MapReduce GA/kNN
    Sikana TanupabrungsunT. Achalakul

    Computer Science, Engineering

    2013 International Conference on Parallel and…

  • 2013

The feature reduction technique is proposed, which is designed to identify a subset of informative features that are representatives of the whole dataset and can be reduced by 50% with 83.12% accuracy.

Modified Whale Optimization Algorithm For Feature Selection In Micro Array Cancer Dataset
    Dr M. SathyaS. Priya

    Computer Science, Medicine

  • 2020

For cancer detection, a modified whale optimized algorithm is proposed to select feature genes from the microarray dataset to help in dimensionality reduction of the dataset and target genes that are responsible for causing cancer.

  • 2
  • PDF
An optimal structure for ensemble feature selection
    A. RouhiH. Nezamabadi-pour

    Computer Science

  • 2020

The purpose of this paper is to find an optimal structure for hybrid-ensemble gene selection method that, by selecting the least number of the genes, yields the desired classification accuracy.

  • 1
Feature selection using Artificial Bee Colony for cardiovascular disease classification
    B. SubanyaR. Rajalaxmi

    Medicine, Computer Science

    2014 International Conference on Electronics and…

  • 2014

The main objective of this paper is to use a metaheuristic algorithm to determine the optimal feature subset with improved classification accuracy in cardiovascular disease diagnosis and the results show that, ABC-SVM performs better than Feature selection with reverse ranking.

  • 53
Distributed Feature Selection for Efficient Economic Big Data Analysis
    Liang ZhaoZhikui ChenYueming HuGeyong MinZhaohua Jiang

    Economics, Computer Science

    IEEE Transactions on Big Data

  • 2018

A new framework for efficient analysis of high-dimensional economic big data based on innovative distributed feature selection and econometric model construction to reveal the hidden patterns for economic development is presented.

  • 55
  • PDF
A hybrid approach of differential evolution and artificial bee colony for feature selection
    Ezgi ZorarpacıS. A. Özel

    Computer Science

    Expert Syst. Appl.

  • 2016
  • 306
Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier
    Sushruta MishraP. MallickHrudaya Kumar TripathyAkash Kumar BhoiAlfonso González-Briones

    Medicine, Computer Science

    Applied Sciences

  • 2020

A new hybrid Attribute Evaluator method has been proposed which effectively integrates enhanced K-Means clustering with the CFS filter method and the BFS wrapper method and was evaluated with an improved decision tree classifier.



52 References

Feature selection environment for genomic applications
    Fabricio M. LopesD. MartinsR. M. C. Junior

    Biology, Computer Science

    BMC Bioinformatics

  • 2008

The intent of this work is to provide an open-source multiplataform graphical environment for bioinformatics problems, which supports many feature selection algorithms, criterion functions and graphic visualization tools such as scatterplots, parallel coordinates and graphs.

Tumor classification by gene expression profiling: comparison and validation of five clustering methods
    M. GranzowD. BerrarW. DubitzkyA. SchusterF. AzuajeR. Eils

    Biology, Computer Science


  • 2001

This work describes a comparative study of five clustering methods for microarray gene expression profiling of tumors, a computing methodology that discovers and describes meaningful patterns or structures in data.

  • 51
A Survey of Feature Selection Techniques
    Barak ChiziL. RokachO. Maimon

    Computer Science

    Encyclopedia of Data Warehousing and Mining

  • 2009

The objective of Feature Selection is to identify features in the data-set as important, and discard any other feature as irrelevant and redundant information, since Feature Selection reduces the dimensionality of the data.

  • 58
Improved binary PSO for feature selection using gene expression data
    Li-Yeh ChuangHsueh-Wei ChangChung-Jui TuCheng-Hong Yang

    Computer Science, Medicine

    Comput. Biol. Chem.

  • 2008
  • 538
Penalized feature selection and classification in bioinformatics
    Shuangge MaJian Huang

    Computer Science, Biology

    Briefings Bioinform.

  • 2008

This article provides a review of several recently developed penalized feature selection and classification techniques--which belong to the family of embedded feature selection methods--for bioinformatics studies with high-dimensional input.

  • 256
  • PDF
Data dimensionality reduction based on genetic selection of feature subsets
    K. FaraounA. Rabhi

    Computer Science

  • 2015

It is shown that a multi-classification process can be significantly enhanced by selecting an optimal set of the features used as input for the training operation, and that the proposed approach can enhance both the classification rate and the learning runtime.

  • 18
Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction
    N. PochetF. SmetJ. SuykensB. Moor

    Computer Science


  • 2004

A systematic benchmarking study comparing linear versions of standard classification and dimensionality reduction techniques with their non-linear versions based on non- linear kernel functions with a radial basis function (RBF) kernel finds that Kernel PCA with linear kernel gives better results.

  • 205
  • PDF
An Independent Rough Set Approach Hybrid with Artificial Bee Colony Algorithm for Dimensionality Reduction
    N. SugunaK. Thanushkodi

    Computer Science

  • 2011

An improved Rough Set-based Attribute Attribute Reduction (RSAR) namely Independent RSAR hybrid with Artificial Bee Colony (ABC) algorithm, which finds the subset of attributes independently based on decision attributes at first and then finds the final reduct.

  • 61
  • PDF
Gene expression data classification using locally linear discriminant embedding
    B. LiC. ZhengDe-shuang HuangLei ZhangKyungsook Han

    Biology, Computer Science

    Comput. Biol. Medicine

  • 2010
  • 43
  • PDF
Text feature selection using ant colony optimization
    Mehdi Hosseinzadeh AghdamN. Ghasem-AghaeeMohammad Ehsan Basiri

    Computer Science

    Expert Syst. Appl.

  • 2009
  • 392



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