A Proximity Weighted Evidential k Nearest Neighbor Classifier for Imbalanced Data

Pritom Saha Akash; Sadia Sharmin; Amin Ahsan Ali; Mohammad Shoyaib

doi:10.1007/978-3-030-47436-2_6

Journal Article2020Cited by 19

A Proximity Weighted Evidential k Nearest Neighbor Classifier for Imbalanced Data

Pritom Saha Akash, Sadia Sharmin, Amin Ahsan Ali, Mohammad Shoyaib

Advances in Knowledge Discovery and Data Mining

Springer International Publishing, pp. 71-83, ISBN: 9783030474355

DOI: 10.1007/978-3-030-47436-2_6 Open Access URL

Keywords

Computer sciencePairwise comparisonk-nearest neighbors algorithmClassifier (UML)Artificial intelligencePattern recognition (psychology)Nearest neighbor searchNearest neighbourData miningBenchmark (surveying)Large margin nearest neighborMachine learning

CCDS Authors

Amin Ahsan Ali, PhD

Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Research Interests

Artificial Intelligence, Machine Learning

References

1.N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–357[10.1613/jair.953]
2.Arthur Asuncion. (2007). UCI Machine Learning Repository. Medical Entomology and Zoology
3.Glenn Shafer. (2020). A Mathematical Theory of Evidence. Princeton University Press eBooks[10.2307/j.ctv10vm1qb]
4.John A. Swets. (1988). Measuring the Accuracy of Diagnostic Systems. Science, 240(4857), 1285–1293[10.1126/science.3287615]
5.Glenn Shafer. (1976). A Mathematical Theory of Evidence. Princeton University Press eBooks[10.1515/9780691214696]
6.Geoffrey McLachlan, David Peel. (2000). Finite Mixture Models. Wiley series in probability and statistics, 6(1), 355–378[10.1002/0471721182]
7.Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, Dan Steinberg. (2007). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37[10.1007/s10115-007-0114-2]
8.Evelyn Fix, J. L. Hodges. (1989). Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties. International Statistical Review, 57(3), 238[10.2307/1403797]
9.Jesús Alcalá‐Fdez, Alberto Fernández, Julián Luengo, J. Derrac, Salvador García, Luciano Sánchez, Francisco Herrera. (2011). KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework. , 17, 255–287
10.Sahibsingh A. Dudani. (1976). The Distance-Weighted k-Nearest-Neighbor Rule. IEEE Transactions on Systems Man and Cybernetics, SMC-6(4), 325–327[10.1109/tsmc.1976.5408784]
11.T. Denoeux. (1995). A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems Man and Cybernetics, 25(5), 804–813[10.1109/21.376493]
12.QIANG YANG, XINDONG WU. (2006). 10 CHALLENGING PROBLEMS IN DATA MINING RESEARCH. International Journal of Information Technology & Decision Making, 05(04), 597–604[10.1142/s0219622006002258]
13.Thierry Denœux. (2008). A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory. Studies in fuzziness and soft computing, 737–760[10.1007/978-3-540-44792-4_29]
14.Jianping Gou, Hongxing Ma, Weihua Ou, Shaoning Zeng, Yunbo Rao, Hebiao Yang. (2018). A generalized mean distance-based k-nearest neighbor classifier. Expert Systems with Applications, 115, 356–372[10.1016/j.eswa.2018.08.021]
15.David A. Cieslak, Nitesh V. Chawla. (2008). Learning Decision Trees for Unbalanced Data. Lecture notes in computer science, 241–256[10.1007/978-3-540-87479-9_34]
16.Wei Liu, Sanjay Chawla, David A. Cieslak, Nitesh V. Chawla. (2010). A Robust Decision Tree Algorithm for Imbalanced Data Sets. , 766–777[10.1137/1.9781611972801.67]
17.Wei Liu, Sanjay Chawla. (2011). Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets. Lecture notes in computer science, 345–356[10.1007/978-3-642-20847-8_29]
18.Yuxuan Li, Xiuzhen Zhang. (2011). Improving k Nearest Neighbor with Exemplar Generalization for Imbalanced Classification. Lecture notes in computer science, 321–332[10.1007/978-3-642-20847-8_27]
19.Harshit Dubey, Vikram Pudi. (2013). Class Based Weighted K-Nearest Neighbor over Imbalance Dataset. Lecture notes in computer science, 305–316[10.1007/978-3-642-37456-2_26]
20.Pritom Saha Akash, Md. Eusha Kadir, Amin Ahsan Ali, Mohammad Shoyaib. (2019). Inter-node Hellinger Distance based Decision Tree. , 1967–1973[10.24963/ijcai.2019/272]
21.Theodore B. Trafalis, Samir A. Alwazzi. (2007). Support vector regression with noisy data: a second order cone programming approach. International Journal of General Systems, 36(2), 237–250[10.1080/03081070601058760]
22.Evelyn Fix, J. L. Hodges. (1951). Discriminatory analysis: Nonparametric discrimination: Consistency properties. PsycEXTRA Dataset[10.1037/e471672008-001]

Cited By

1.Marcio Dorn, Bruno Iochins Grisci, Pedro Henrique Narloch, Bruno César Feltes, Eduardo Avila, Alessandro Kahmann, Clarice Sampaio Alho. (2021). Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets. PeerJ Computer Science, 7, e670[10.7717/peerj-cs.670]
2.Ali Fazli, Javad Poshtan. (2024). Wind turbine fault detection and isolation robust against data imbalance using KNN. Energy Science & Engineering, 12(3), 1174–1186[10.1002/ese3.1706]
3.Md Nazmul Haque, Sadia Sharmin, Amin Ahsan Ali, Abu Ashfaqur Sajib, Mohammad Shoyaib. (2021). Use of relevancy and complementary information for discriminatory gene selection from high-dimensional gene expression data. PLoS ONE, 16(10), e0230164[10.1371/journal.pone.0230164]
4.Suravi Akhter, Sadia Sharmin, Sumon Ahmed, Abu Ashfaqur Sajib, Mohammad Shoyaib. (2021). mRelief: A Reward Penalty Based Feature Subset Selection Considering Data Overlapping Problem. Lecture notes in computer science, 278–292[10.1007/978-3-030-77961-0_24]
5.Md. Hasan Tarek, Md. Mumtahin Habib Ullah Mazumder, Sadia Sharmin, Md. Shariful Islam, Mohammad Shoyaib, Muhammad Mahbub Alam. (2022). RHC: Cluster based Feature Reduction for Network Intrusion Detections. , 15, 378–384[10.1109/ccnc49033.2022.9700680]
6.Pablo Ormeño, Gastón Márquez, Carla Taramasco. (2024). Evaluation of Machine Learning Techniques for Classifying and Balancing Data on an Unbalanced Mini-Mental State Examination Test Data Collection Applied in Chile. IEEE Access, 12, 49376–49386[10.1109/access.2024.3383837]
7.Hüseyin Ünlü, Ozan Raşit Yürüm, Ali Yıldız, Onur Demirörs. (2024). Application of a Size Measurement Standard for Data Warehouse Projects. Software Practice and Experience, 55(3), 571–588[10.1002/spe.3391]
8.Arnab Mukherjee, Md. Zahidul Islam Islam, Lasker Ershad Ali. (2024). Human iris classification through Histogram of Oriented Gradient features with various distance metrics. Machine Graphics and Vision, 33(3/4), 97–124[10.22630/mgv.2024.33.3.5]
9.Bidyapati Thiyam, Chadalavada Suptha Saranya, Shouvik Dey. (2024). Class‐Imbalanced Problems in Malware Analysis and Detection in Classification Algorithms. , 61–81[10.1002/9781394230600.ch4]
10.Anh Hoang, Toan Nguyen Mau, Van-Nam Huynh. (2021). Mass-Based Similarity Weighted k-Neighbor for Class Imbalance. Lecture notes in computer science, 143–155[10.1007/978-3-030-85529-1_12]
11.Tom Wilson, Samuel Wisdish, Josh Osofa, Dominic J. Farris. (2025). Evaluating Machine Learning-Based Classification of Human Locomotor Activities for Exoskeleton Control Using Inertial Measurement Unit and Pressure Insole Data. Sensors, 25(17), 5365[10.3390/s25175365]
12.(2021). Peer Review #1 of "Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets (v0.2)". [10.7287/peerj-cs.670v0.2/reviews/1]
13.Dina Shehada, Suadad Muammar, Maryam Ali Amour, Ahmed Bouridane. (2023). Investigating The Impact of Sampling Techniques on an Imbalanced Classification Problem. , 39–44[10.1109/icspis60075.2023.10344073]
14.(2021). Peer Review #3 of "Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets (v0.1)". [10.7287/peerj-cs.670v0.1/reviews/3]
15.(2021). Peer Review #1 of "Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets (v0.1)". [10.7287/peerj-cs.670v0.1/reviews/1]
16.(2021). Peer Review #3 of "Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets (v0.2)". [10.7287/peerj-cs.670v0.2/reviews/3]
17.Peter Domjan, Viola Angyal, Adam Bertalan, Istvan Vingender, Elek Dinya. (2025). A New Bayesian Approach to Increase Measurement Accuracy Using a Precision Entropy Indicator. Research Square[10.21203/rs.3.rs-5926277/v1]
18.Md. Eusha Kadir, Md. Mumtahin Habib Ullah Mazumder, Muhammad Mahbub Alam, Mohammad Shoyaib. (2026). An Evidential Dynamic Neighborhood Approach to Learn from Imbalanced Data. Annals of Data Science[10.1007/s40745-026-00709-0]
19.P Gonzalez-Dias. (2021). Peer Review #2 of "Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets (v0.2)". [10.7287/peerj-cs.670v0.2/reviews/2]

← All publications

References

1.N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–357[10.1613/jair.953]

2.Arthur Asuncion. (2007). UCI Machine Learning Repository. Medical Entomology and Zoology

3.Glenn Shafer. (2020). A Mathematical Theory of Evidence. Princeton University Press eBooks[10.2307/j.ctv10vm1qb]

4.John A. Swets. (1988). Measuring the Accuracy of Diagnostic Systems. Science, 240(4857), 1285–1293[10.1126/science.3287615]

5.Glenn Shafer. (1976). A Mathematical Theory of Evidence. Princeton University Press eBooks[10.1515/9780691214696]

6.Geoffrey McLachlan, David Peel. (2000). Finite Mixture Models. Wiley series in probability and statistics, 6(1), 355–378[10.1002/0471721182]

7.Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, Dan Steinberg. (2007). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37[10.1007/s10115-007-0114-2]

8.Evelyn Fix, J. L. Hodges. (1989). Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties. International Statistical Review, 57(3), 238[10.2307/1403797]

9.Jesús Alcalá‐Fdez, Alberto Fernández, Julián Luengo, J. Derrac, Salvador García, Luciano Sánchez, Francisco Herrera. (2011). KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework. , 17, 255–287

10.Sahibsingh A. Dudani. (1976). The Distance-Weighted k-Nearest-Neighbor Rule. IEEE Transactions on Systems Man and Cybernetics, SMC-6(4), 325–327[10.1109/tsmc.1976.5408784]

11.T. Denoeux. (1995). A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems Man and Cybernetics, 25(5), 804–813[10.1109/21.376493]

12.QIANG YANG, XINDONG WU. (2006). 10 CHALLENGING PROBLEMS IN DATA MINING RESEARCH. International Journal of Information Technology & Decision Making, 05(04), 597–604[10.1142/s0219622006002258]

13.Thierry Denœux. (2008). A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory. Studies in fuzziness and soft computing, 737–760[10.1007/978-3-540-44792-4_29]

14.Jianping Gou, Hongxing Ma, Weihua Ou, Shaoning Zeng, Yunbo Rao, Hebiao Yang. (2018). A generalized mean distance-based k-nearest neighbor classifier. Expert Systems with Applications, 115, 356–372[10.1016/j.eswa.2018.08.021]

15.David A. Cieslak, Nitesh V. Chawla. (2008). Learning Decision Trees for Unbalanced Data. Lecture notes in computer science, 241–256[10.1007/978-3-540-87479-9_34]

16.Wei Liu, Sanjay Chawla, David A. Cieslak, Nitesh V. Chawla. (2010). A Robust Decision Tree Algorithm for Imbalanced Data Sets. , 766–777[10.1137/1.9781611972801.67]

17.Wei Liu, Sanjay Chawla. (2011). Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets. Lecture notes in computer science, 345–356[10.1007/978-3-642-20847-8_29]

18.Yuxuan Li, Xiuzhen Zhang. (2011). Improving k Nearest Neighbor with Exemplar Generalization for Imbalanced Classification. Lecture notes in computer science, 321–332[10.1007/978-3-642-20847-8_27]

19.Harshit Dubey, Vikram Pudi. (2013). Class Based Weighted K-Nearest Neighbor over Imbalance Dataset. Lecture notes in computer science, 305–316[10.1007/978-3-642-37456-2_26]

20.Pritom Saha Akash, Md. Eusha Kadir, Amin Ahsan Ali, Mohammad Shoyaib. (2019). Inter-node Hellinger Distance based Decision Tree. , 1967–1973[10.24963/ijcai.2019/272]

21.Theodore B. Trafalis, Samir A. Alwazzi. (2007). Support vector regression with noisy data: a second order cone programming approach. International Journal of General Systems, 36(2), 237–250[10.1080/03081070601058760]

22.Evelyn Fix, J. L. Hodges. (1951). Discriminatory analysis: Nonparametric discrimination: Consistency properties. PsycEXTRA Dataset[10.1037/e471672008-001]

Cited By

1.Marcio Dorn, Bruno Iochins Grisci, Pedro Henrique Narloch, Bruno César Feltes, Eduardo Avila, Alessandro Kahmann, Clarice Sampaio Alho. (2021). Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets. PeerJ Computer Science, 7, e670[10.7717/peerj-cs.670]

2.Ali Fazli, Javad Poshtan. (2024). Wind turbine fault detection and isolation robust against data imbalance using KNN. Energy Science & Engineering, 12(3), 1174–1186[10.1002/ese3.1706]

3.Md Nazmul Haque, Sadia Sharmin, Amin Ahsan Ali, Abu Ashfaqur Sajib, Mohammad Shoyaib. (2021). Use of relevancy and complementary information for discriminatory gene selection from high-dimensional gene expression data. PLoS ONE, 16(10), e0230164[10.1371/journal.pone.0230164]

4.Suravi Akhter, Sadia Sharmin, Sumon Ahmed, Abu Ashfaqur Sajib, Mohammad Shoyaib. (2021). mRelief: A Reward Penalty Based Feature Subset Selection Considering Data Overlapping Problem. Lecture notes in computer science, 278–292[10.1007/978-3-030-77961-0_24]

5.Md. Hasan Tarek, Md. Mumtahin Habib Ullah Mazumder, Sadia Sharmin, Md. Shariful Islam, Mohammad Shoyaib, Muhammad Mahbub Alam. (2022). RHC: Cluster based Feature Reduction for Network Intrusion Detections. , 15, 378–384[10.1109/ccnc49033.2022.9700680]

6.Pablo Ormeño, Gastón Márquez, Carla Taramasco. (2024). Evaluation of Machine Learning Techniques for Classifying and Balancing Data on an Unbalanced Mini-Mental State Examination Test Data Collection Applied in Chile. IEEE Access, 12, 49376–49386[10.1109/access.2024.3383837]

7.Hüseyin Ünlü, Ozan Raşit Yürüm, Ali Yıldız, Onur Demirörs. (2024). Application of a Size Measurement Standard for Data Warehouse Projects. Software Practice and Experience, 55(3), 571–588[10.1002/spe.3391]

8.Arnab Mukherjee, Md. Zahidul Islam Islam, Lasker Ershad Ali. (2024). Human iris classification through Histogram of Oriented Gradient features with various distance metrics. Machine Graphics and Vision, 33(3/4), 97–124[10.22630/mgv.2024.33.3.5]

9.Bidyapati Thiyam, Chadalavada Suptha Saranya, Shouvik Dey. (2024). Class‐Imbalanced Problems in Malware Analysis and Detection in Classification Algorithms. , 61–81[10.1002/9781394230600.ch4]

10.Anh Hoang, Toan Nguyen Mau, Van-Nam Huynh. (2021). Mass-Based Similarity Weighted k-Neighbor for Class Imbalance. Lecture notes in computer science, 143–155[10.1007/978-3-030-85529-1_12]

11.Tom Wilson, Samuel Wisdish, Josh Osofa, Dominic J. Farris. (2025). Evaluating Machine Learning-Based Classification of Human Locomotor Activities for Exoskeleton Control Using Inertial Measurement Unit and Pressure Insole Data. Sensors, 25(17), 5365[10.3390/s25175365]

12.(2021). Peer Review #1 of "Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets (v0.2)". [10.7287/peerj-cs.670v0.2/reviews/1]

13.Dina Shehada, Suadad Muammar, Maryam Ali Amour, Ahmed Bouridane. (2023). Investigating The Impact of Sampling Techniques on an Imbalanced Classification Problem. , 39–44[10.1109/icspis60075.2023.10344073]

14.(2021). Peer Review #3 of "Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets (v0.1)". [10.7287/peerj-cs.670v0.1/reviews/3]

15.(2021). Peer Review #1 of "Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets (v0.1)". [10.7287/peerj-cs.670v0.1/reviews/1]

16.(2021). Peer Review #3 of "Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets (v0.2)". [10.7287/peerj-cs.670v0.2/reviews/3]

17.Peter Domjan, Viola Angyal, Adam Bertalan, Istvan Vingender, Elek Dinya. (2025). A New Bayesian Approach to Increase Measurement Accuracy Using a Precision Entropy Indicator. Research Square[10.21203/rs.3.rs-5926277/v1]

18.Md. Eusha Kadir, Md. Mumtahin Habib Ullah Mazumder, Muhammad Mahbub Alam, Mohammad Shoyaib. (2026). An Evidential Dynamic Neighborhood Approach to Learn from Imbalanced Data. Annals of Data Science[10.1007/s40745-026-00709-0]

19.P Gonzalez-Dias. (2021). Peer Review #2 of "Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets (v0.2)". [10.7287/peerj-cs.670v0.2/reviews/2]