Dimensionality Reduction Techniques and their Applications in Cancer Classification: A Comprehensive Review

Year : 2023 | Volume : 01 | Issue : 02 | Page : 35-45

    Abrar Yaqoob

  1. Mohd Abas Bhat

  2. Zeba Khan

  1. Research Scholar, Department of Mathematics VIT Bhopal, Sehore, Madhya Pradesh, India
  2. Student, Department of Economics, Kashmir University, Srinagar, Jammu, India
  3. Research Scholar, Department of biotech, vit university Bhopal, Sehore, Madhya Pradesh, India


Dimensionality reduction techniques have become a vital tool in the investigation of high-dimensional data like gene expression profiles in cancer research. Here is a review, we deliver a comprehensive overview of dimensionality reduction techniques and their applications in cancer classification. Firstly, we introduce the concepts and approaches of dimensionality reduction, and after that, we explore several methods for decreasing dimensionality. These techniques include Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), and t-Distributed Stochastic Neighbour Embedding (t-SNE). We then present a comprehensive review of applications of these techniques in cancer classification, counting lung cancer, colon cancer, breast cancer, and leukemia. Moreover, we discuss the advantages and disadvantages of different dimensionality reduction techniques in cancer classification, as well as their limitations and future directions. Finally, we summarize the most recent stage in the area and make it available for use of some recommendations for future studies. Overall, this review highlights the importance of dimensionality reduction techniques in classification of cancer and provides a valuable resource for researchers working in this field.

Keywords: Feature extraction, cancer classification, dimensionality reduction, feature selection

[This article belongs to International Journal of Genetic Modifications and Recombinations(ijgmr)]

How to cite this article: Abrar Yaqoob, Mohd Abas Bhat, Zeba Khan Dimensionality Reduction Techniques and their Applications in Cancer Classification: A Comprehensive Review ijgmr 2023; 01:35-45
How to cite this URL: Abrar Yaqoob, Mohd Abas Bhat, Zeba Khan Dimensionality Reduction Techniques and their Applications in Cancer Classification: A Comprehensive Review ijgmr 2023 {cited 2023 Oct 25};01:35-45. Available from: https://journals.stmjournals.com/ijgmr/article=2023/view=125593/

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[1]      R. Aziz, C. K. Verma, and N. Srivastava, “Artificial Neural Network Classification of High Dimensional Data with Novel Optimization Approach of Dimension Reduction,” Ann. Data Sci., vol. 5, no. 4, pp. 615–635, Dec. 2018, doi: 10.1007/s40745-018-0155-2.

[2]      S. Ayesha, M. K. Hanif, and R. Talib, “Overview and comparative study of dimensionality reduction techniques for high dimensional data,” Inf. Fusion, vol. 59, no. January, pp. 44–58, 2020, doi: 10.1016/j.inffus.2020.01.005.

[3]      L. J. P. Van Der Maaten, E. O. Postma, and H. J. Van Den Herik, “Dimensionality Reduction: A Comparative Review,” J. Mach. Learn. Res., vol. 10, pp. 1–41, 2009, doi: 10.1080/13506280444000102.

[4]      U. N. Wisesty, E. Lisnawati, A. Aditsania, and D. S. Kusumo, “Dimensionality Reduction using Principal Component Analysis for Cancer Detection based on Microarray Data Classification Dimensionality Reduction using Principal Component Analysis for Cancer Detection based on Microarray Data Classification,” no. November, 2018, doi: 10.3844/jcssp.2018.1521.1530.

[5]      I. Guyon, “Gene Selection for Cancer Classification,” pp. 389–422, 2002.

[6]      H. Lu, J. Chen, K. Yan, Q. Jin, Y. Xue, and Z. Gao, “A hybrid feature selection algorithm for gene expression data classification,” Neurocomputing, vol. 256, pp. 56–62, Sep. 2017, doi: 10.1016/j.neucom.2016.07.080.

[7]      F. Murtagh, “A survey of recent advances in hierarchical clustering algorithms,” Comput. J., vol. 26, no. 4, pp. 354–359, 1983, doi: 10.1093/comjnl/26.4.354.

[8]      S. Shukla and S. Naganna, “A Review ON K-means DATA Clustering APPROACH,” vol. 4, no. 17, pp. 1847–1860, 2014.

[9]      A. Bommert, X. Sun, B. Bischl, J. Rahnenführer, and M. Lang, “Benchmark for filter methods for feature selection in high-dimensional classification data,” Comput. Stat. Data Anal., vol. 143, p. 106839, 2020, doi: 10.1016/j.csda.2019.106839.

[10]    M. Jansi Rani and D. Devaraj, “Two-Stage Hybrid Gene Selection Using Mutual Information and Genetic Algorithm for Cancer Data Classification,” J. Med. Syst., vol. 43, no. 8, Aug. 2019, doi: 10.1007/s10916-019-1372-8.

[11]    O. Ahmad Alomari, A. Tajudin Khader, M. Azmi Al-Betar, and L. Mohammad Abualigah, “Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm,” Int. J. Data Min. Bioinform., vol. 19, no. 1, pp. 32–51, 2017, doi: 10.1504/IJDMB.2017.088538.

[12]    J. Galon et al., “Cancer classification using the Immunoscore: A worldwide task force,” J. Transl. Med., vol. 10, no. 1, 2012, doi: 10.1186/1479-5876-10-205.

[13]    N. Almugren and H. Alshamlan, “A survey on hybrid feature selection methods in microarray gene expression data for cancer classification,” IEEE Access, vol. 7, pp. 78533–78548, 2019, doi: 10.1109/ACCESS.2019.2922987.

[14]    V. Elyasigomari, D. A. Lee, H. R. C. Screen, and M. H. Shaheed, “Development of a two-stage gene selection method that incorporates a novel hybrid approach using the cuckoo optimization algorithm and harmony search for cancer classification,” J. Biomed. Inform., vol. 67, pp. 11–20, 2017, doi: 10.1016/j.jbi.2017.01.016.

[15]    I. Jain, V. K. Jain, and R. Jain, “Correlation feature selection based improved-Binary Particle Swarm Optimization for gene selection and cancer classification,” Appl. Soft Comput., vol. 62, pp. 203–215, 2018, doi: 10.1016/j.asoc.2017.09.038.

[16]    G. Nguyen et al., “Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey,” Artif. Intell. Rev., vol. 52, no. 1, pp. 77–124, 2019, doi: 10.1007/s10462-018-09679-z.

[17]    S. Cho and H. Won, “Machine Learning in DNA Microarray Analysis for Cancer Classification,” no. May 2014, 2018.

[18]    N. Almugren and H. Alshamlan, “A survey on hybrid feature selection methods in microarray gene expression data for cancer classification,” IEEE Access, vol. 7. Institute of Electrical and Electronics Engineers Inc., pp. 78533–78548, 2019. doi: 10.1109/ACCESS.2019.2922987.

[19]    A. Yaqoob, R. M. Aziz, N. K. Verma, P. Lalwani, and A. Makrariya, “A Review on Nature-Inspired Algorithms for Cancer Disease Prediction and Classification,” 2023.

[20]    A. Ghodsi, “Dimensionality Reduction A Short Tutorial.”

[21]    A. Jović, K. Brkić, and N. Bogunović, “A review of feature selection methods with applications,” 2015 38th Int. Conv. Inf. Commun. Technol. Electron. Microelectron. MIPRO 2015 – Proc., no. May 2015, pp. 1200–1205, 2015, doi: 10.1109/MIPRO.2015.7160458.

[22]    D. A. A. Gnana, “Literature Review on Feature Selection Methods for High-Dimensional Data Literature Review on Feature Selection Methods for High-Dimensional Data,” no. August, 2016, doi: 10.5120/ijca2016908317.

[23]    C. C. Aggarwal, “Educational and software resources for data classification,” Data Classif. Algorithms Appl., pp. 657–665, 2014, doi: 10.1201/b17320.

[24]    V. Bolón-Canedo, N. Sánchez-Maroño, and A. Alonso-Betanzos, “A review of feature selection methods on synthetic data,” Knowl. Inf. Syst., vol. 34, no. 3, pp. 483–519, 2013, doi: 10.1007/s10115-012-0487-8.

[25]    G. Chandrashekar and F. Sahin, “A survey on feature selection methods,” Comput. Electr. Eng., vol. 40, no. 1, pp. 16–28, 2014, doi: 10.1016/j.compeleceng.2013.11.024.

[26]    B. Remeseiro and V. Bolon-Canedo, “A review of feature selection methods in medical applications,” Comput. Biol. Med., vol. 112, no. July, p. 103375, 2019, doi: 10.1016/j.compbiomed.2019.103375.

[27]    H. J. Ferreau et al., “Embedded Optimization Methods for Industrial Automatic Control,” vol. 50, no. 1, pp. 13194–13209, 2017, doi: 10.1016/j.ifacol.2017.08.1946.

[28]    I. Gitchat, “特征选择 ( Feature Selection ) 特征选择 Feature Selection,” Comput. Vis., vol. 392, no. March, pp. 1–10, 2018, [Online]. Available: http://link.springer.com/10.1007/978-3-030-03243-2_299-1

[29]    P. Lamba and K. Rawal, “A Survey of Algorithms for Feature Extraction and Feature Classification Methods.”

[30]    F. Roberti de Siqueira, W. Robson Schwartz, and H. Pedrini, “Multi-scale gray level co-occurrence matrices for texture description,” Neurocomputing, vol. 120, pp. 336–345, 2013, doi: 10.1016/j.neucom.2012.09.042.

[31]    A. Hadid, J. Ylioinas, M. Bengherabi, M. Ghahramani, and A. Taleb-Ahmed, “Gender and texture classification: A comparative analysis using 13 variants of local binary patterns,” Pattern Recognit. Lett., vol. 68, pp. 231–238, 2015, doi: 10.1016/j.patrec.2015.04.017.

[32]    Á. Serrano, I. M. de Diego, C. Conde, and E. Cabello, “Recent advances in face biometrics with Gabor wavelets: A review,” Pattern Recognit. Lett., vol. 31, no. 5, pp. 372–381, 2010, doi: 10.1016/j.patrec.2009.11.002.

[33]    S. E. Lee, K. Min, and T. Suh, “Accelerating Histograms of Oriented Gradients descriptor extraction for pedestrian recognition,” Comput. Electr. Eng., vol. 39, no. 4, pp. 1043–1048, 2013, doi: 10.1016/j.compeleceng.2013.04.001.

[34]    N. Aloysius, A. V. Vidyapeetham, G. Madathilkulangara, and A. V. Vidyapeetham, “A Review on Deep Convolutional Neural Networks,” no. April, 2017, doi: 10.1109/ICCSP.2017.8286426.

[35]    I. Guyon, S. Gunn, and M. Nikravesh, “Feature Extraction,” 2006.

[36]    J. Behmann, A. Mahlein, T. Rumpf, C. Ro, and L. Plu, “A review of advanced machine learning methods for the detection of biotic stress in precision crop protection,” pp. 239–260, 2015, doi: 10.1007/s11119-014-9372-7.

[37]    K. K. Kumar, K. Chaduvula, and B. R. Markapudi, “A Detailed Survey On Feature Extraction Techniques In Image Processing For Medical Image Analysis,” vol. 07, no. 10, pp. 2275–2284, 2020.

[38]    K. L. Tang, T. H. Li, W. W. Xiong, and K. Chen, “Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data,” BMC Bioinformatics, vol. 11, 2010, doi: 10.1186/1471-2105-11-109.

[39]    M. F. Kabir, T. Chen, and S. A. Ludwig, “A performance analysis of dimensionality reduction algorithms in machine learning models for cancer prediction,” Healthc. Anal., vol. 3, no. November 2022, p. 100125, 2023, doi: 10.1016/j.health.2022.100125.

[40]    M. Nilashi, O. Ibrahim, H. Ahmadi, and L. Shahmoradi, “A knowledge-based system for breast cancer classification using fuzzy logic method,” Telemat. Informatics, vol. 34, no. 4, pp. 133–144, 2017, doi: 10.1016/j.tele.2017.01.007.

[41]    S. M. Ayyad, A. I. Saleh, and L. M. Labib, “Gene expression cancer classification using modified K-Nearest Neighbors technique,” BioSystems, vol. 176, no. January, pp. 41–51, 2019, doi: 10.1016/j.biosystems.2018.12.009.

[42]    H. Salem, G. Attiya, and N. El-Fishawy, “Classification of human cancer diseases by gene expression profiles,” Appl. Soft Comput. J., vol. 50, pp. 124–134, 2017, doi: 10.1016/j.asoc.2016.11.026.

[43]    M. Dashtban and M. Balafar, “Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts,” Genomics, vol. 109, no. 2, pp. 91–107, 2017, doi: 10.1016/j.ygeno.2017.01.004.

[44]    S. F. Abdoh, M. Abo Rizka, and F. A. Maghraby, “Cervical cancer diagnosis using random forest classifier with SMOTE and feature reduction techniques,” IEEE Access, vol. 6, pp. 59475–59485, 2018, doi: 10.1109/ACCESS.2018.2874063.

[45]    A. Madduri, S. S. Adusumalli, H. S. Katragadda, M. K. R. Dontireddy, and P. S. Suhasini, “Classification of Breast Cancer Histopathological Images using Convolutional Neural Networks,” Proc. 8th Int. Conf. Signal Process. Integr. Networks, SPIN 2021, pp. 755–759, 2021, doi: 10.1109/SPIN52536.2021.9566015.

[46]    R. Yan et al., “Breast cancer histopathological image classification using a hybrid deep neural network,” Methods, vol. 173, no. June 2019, pp. 52–60, 2020, doi: 10.1016/j.ymeth.2019.06.014.

Regular Issue Subscription Original Research
Volume 01
Issue 02
Received September 13, 2023
Accepted April 29, 2023
Published October 25, 2023