在半導體製造產業中，高良率是維持市場競爭力的重要因素之一。隨著半導體技術演進，使用傳統的統計管制手法進行品質管控已無法負擔日趨複雜、多樣化的製程，無法即時發現產線異常會導致額外的生產成本，也影響客戶的合作意願。面對半導體製造過程中產生的大量高維度、非線性、不平衡資料，需要跳脫傳統，引入機器學習方法，透過非線性分類模型達到更即時的異常偵測，並進一步分析異常發生的根本原因，以提前發現產線問題並即時改進。
由於半導體製造的產線資料的特徵數量相當高，需要進行資料降維以減少資料中的雜訊、降低計算成本。而特徵選擇為資料降維的主要方法之一，並進一步區分為過濾器、包裝器與嵌入式方法。基於統計理論的過濾器計算迅速、泛用性高，而基於啟發式演算法的包裝器雖時間複雜度高，但經常能在個案中得到良好的表現，若將兩者進一步結合為混合式方法，能同時滿足資料品質和運算成本。為此，本研究將提出一兩階段特徵選擇模型，首先利用互資訊刪去冗餘特徵，縮小特徵空間後，再以簡化群體演算法，設計獨特的適應度函數以從候選特徵中，挑選出最佳特徵集合，最後選用支援向量機為分類模型進行驗證。從實際案例中可發現，本研究所提的特徵選擇方法，在晶圓異常分類問題中，以較少的特徵數量得到較佳的分類精度。在公開數據集中的表現，也進一步佐證了此方法的有效性和泛化能力。

High yield is vital for competitiveness in semiconductor manufacturing. Traditional statistical control methods fail to detect real-time production line abnormalities, causing extra costs and hampering customer collaboration. To address the high-dimensional, nonlinear, and imbalanced data in this field, machine learning methods are needed for real-time anomaly detection. And further analysis of the root causes of anomalies allows for early detection of production line issues and timely improvements.
Facing the high dimensionality of production line data, dimensionality reduction reduces data noises and costs. Feature selection is a primary method for data dimensionality reduction, including Filter, Wrapper, and Embedded methods. Filter methods provide fast computation and generality based on statistical theory, while wrapper methods offer higher accuracy but with higher time complexity. A hybrid approach combines both, addressing data quality and computational costs. In this paper, we proposes a two-stage feature selection model , starting with the elimination of redundant features using mutual information to reduce the feature space. Then, a Simplified Swarm Optimization algorithm is used to select the optimal feature subset from the candidate features. Finally, support vector machines are employed as the classification model for validation. In wafer anomaly classification case study, results show improved accuracy with fewer features. And the performance on public datasets further validates the effectiveness and generalization capability of this method.

摘要 i
Abstract ii
目錄 iii
表目錄 iv
圖目錄 v
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 論文結構 5
第二章 文獻回顧 7
2.1 半導體製造異常問題 7
2.2 特徵選擇 12
2.3 分類演算法 15
2.4 簡化群體演算法 20
2.5 不平衡分類問題 21
2.6 文獻回顧小結 24
第三章 研究方法 25
3.1 互資訊 25
3.2 基於SSO的特徵選擇 26
第四章 實驗結果與分析 30
4.1 資料集說明 30
4.2 MI-SSO參數設定 31
4.3 實驗結果 34
4.4 個案驗證 37
第五章 結論與未來展望 40
5.1 結論 40
5.2 後續研究方向 40

[1] G. E. Moore, "Cramming more components onto integrated circuits (Reprinted from Electronics, pg 114-117, April 19, 1965)," Proceedings of the Ieee, vol. 86, no. 1, pp. 82-85, Jan 1998, doi: 10.1109/jproc.1998.658762.
[2] C. A. Mack, "Fifty years of Moore's law," IEEE Transactions on semiconductor manufacturing, vol. 24, no. 2, pp. 202-207, 2011.
[3] 菊地正典, 半導體工廠：設備、材料、製程及提升產業復興的處方籤. 世茂出版社, 2016.
[4] S. K. S. Fan, C. W. Cheng, and D. M. Tsai, "Fault Diagnosis of Wafer Acceptance Test and Chip Probing Between Front-End-of-Line and Back-End-of-Line Processes," Ieee Transactions on Automation Science and Engineering, vol. 19, no. 4, pp. 3068-3082, Oct 2022, doi: 10.1109/tase.2021.3106011.
[5] W. H. Woodall, D. J. Spitzner, D. C. Montgomery, and S. Gupta, "Using control charts to monitor process and product quality profiles," Journal of Quality Technology, vol. 36, no. 3, pp. 309-320, 2004.
[6] T. Kourti and J. F. MacGregor, "Process analysis, monitoring and diagnosis, using multivariate projection methods," Chemometrics and intelligent laboratory systems, vol. 28, no. 1, pp. 3-21, 1995.
[7] J. Senoner, T. Netland, and S. Feuerriegel, "Using explainable artificial intelligence to improve process quality: Evidence from semiconductor manufacturing," Management Science, vol. 68, no. 8, pp. 5704-5723, 2022.
[8] Q. P. He and J. Wang, "Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes," IEEE transactions on semiconductor manufacturing, vol. 20, no. 4, pp. 345-354, 2007.
[9] C.-F. Chien, C.-Y. Hsu, and P.-N. Chen, "Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence," Flexible Services and Manufacturing Journal, vol. 25, no. 3, pp. 367-388, 2013.
[10] R. Baly and H. Hajj, "Wafer classification using support vector machines," IEEE Transactions on Semiconductor Manufacturing, vol. 25, no. 3, pp. 373-383, 2012.
[11] M. Piao, C. H. Jin, J. Y. Lee, and J.-Y. Byun, "Decision tree ensemble-based wafer map failure pattern recognition based on radon transform-based features," IEEE Transactions on Semiconductor Manufacturing, vol. 31, no. 2, pp. 250-257, 2018.
[12] C. K. Shin and S. C. Park, "A machine learning approach to yield management in semiconductor manufacturing," International Journal of Production Research, vol. 38, no. 17, pp. 4261-4271, 2000.
[13] K. C.-C. Cheng et al., "Machine learning-based detection method for wafer test induced defects," IEEE Transactions on Semiconductor Manufacturing, vol. 34, no. 2, pp. 161-167, 2021.
[14] V. Bolón-Canedo, N. Sánchez-Maroño, and A. Alonso-Betanzos, Feature selection for high-dimensional data. Springer, 2015.
[15] R. Zebari, A. Abdulazeez, D. Zeebaree, D. Zebari, and J. Saeed, "A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction," Journal of Applied Science and Technology Trends, vol. 1, no. 2, pp. 56-70, 2020.
[16] A. Janecek, W. Gansterer, M. Demel, and G. Ecker, "On the relationship between feature selection and classification accuracy," in New challenges for feature selection in data mining and knowledge discovery, 2008: PMLR, pp. 90-105.
[17] S.-K. S. Fan, C.-W. Cheng, and D.-M. Tsai, "Fault diagnosis of wafer acceptance test and chip probing between front-end-of-line and back-end-of-line processes," IEEE Transactions on Automation Science and Engineering, 2021.
[18] C.-F. Chien, P.-C. Lee, R. Dou, Y.-J. Chen, and C.-C. Chen, "Modeling collinear WATs for parametric yield enhancement in semiconductor manufacturing," in 2017 13th IEEE Conference on Automation Science and Engineering (CASE), 2017: IEEE, pp. 739-743.
[19] B. Venkatesh and J. Anuradha, "A review of feature selection and its methods," Cybernetics and Information Technologies, vol. 19, no. 1, pp. 3-26, 2019.
[20] A. Fernández, S. García, M. Galar, R. C. Prati, B. Krawczyk, and F. Herrera, Learning from imbalanced data sets. Springer, 2018.
[21] H. Kaur, H. S. Pannu, and A. K. Malhi, "A systematic review on imbalanced data challenges in machine learning: Applications and solutions," ACM Computing Surveys (CSUR), vol. 52, no. 4, pp. 1-36, 2019.
[22] D. Jiang, W. Lin, and N. Raghavan, "A Gaussian mixture model clustering ensemble regressor for semiconductor manufacturing final test yield prediction," IEEE Access, vol. 9, pp. 22253-22263, 2021.
[23] S.-K. S. Fan, S.-C. Lin, and P.-F. Tsai, "Wafer fault detection and key step identification for semiconductor manufacturing using principal component analysis, AdaBoost and decision tree," Journal of Industrial and Production Engineering, vol. 33, no. 3, pp. 151-168, 2016.
[24] C.-F. Chien, W.-C. Wang, and J.-C. Cheng, "Data mining for yield enhancement in semiconductor manufacturing and an empirical study," Expert Systems with Applications, vol. 33, no. 1, pp. 192-198, 2007.
[25] G. Pang, C. Shen, L. Cao, and A. V. D. Hengel, "Deep Learning for Anomaly Detection: A Review," ACM Comput. Surv., vol. 54, no. 2, p. Article 38, 2021, doi: 10.1145/3439950.
[26] M.-J. Wu, J.-S. R. Jang, and J.-L. Chen, "Wafer map failure pattern recognition and similarity ranking for large-scale data sets," IEEE Transactions on Semiconductor Manufacturing, vol. 28, no. 1, pp. 1-12, 2014.
[27] M. H. Hansen, V. N. Nair, and D. J. Friedman, "Monitoring wafer map data from integrated circuit fabrication processes for spatially clustered defects," Technometrics, vol. 39, no. 3, pp. 241-253, 1997.
[28] J. Yu and X. Lu, "Wafer map defect detection and recognition using joint local and nonlocal linear discriminant analysis," IEEE Transactions on Semiconductor Manufacturing, vol. 29, no. 1, pp. 33-43, 2015.
[29] T.-J. Hsieh, C.-S. Liao, Y.-S. Huang, and C.-F. Chien, "A new morphology-based approach for similarity searching on wafer bin maps in semiconductor manufacturing," in Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2012: IEEE, pp. 869-874.
[30] C.-S. Liao, T.-J. Hsieh, Y.-S. Huang, and C.-F. Chien, "Similarity searching for defective wafer bin maps in semiconductor manufacturing," IEEE Transactions on Automation Science and Engineering, vol. 11, no. 3, pp. 953-960, 2013.
[31] M. Saqlain, B. Jargalsaikhan, and J. Y. Lee, "A voting ensemble classifier for wafer map defect patterns identification in semiconductor manufacturing," IEEE Transactions on Semiconductor Manufacturing, vol. 32, no. 2, pp. 171-182, 2019.
[32] T. Nakazawa and D. V. Kulkarni, "Wafer map defect pattern classification and image retrieval using convolutional neural network," IEEE Transactions on Semiconductor Manufacturing, vol. 31, no. 2, pp. 309-314, 2018.
[33] B. E. Goodlin, D. S. Boning, H. H. Sawin, and B. M. Wise, "Simultaneous fault detection and classification for semiconductor manufacturing tools," Journal of the Electrochemical Society, vol. 150, no. 12, p. G778, 2003.
[34] S.-K. S. Fan, C.-Y. Hsu, D.-M. Tsai, F. He, and C.-C. Cheng, "Data-driven approach for fault detection and diagnostic in semiconductor manufacturing," IEEE Transactions on Automation Science and Engineering, vol. 17, no. 4, pp. 1925-1936, 2020.
[35] S. J. Hong, W. Y. Lim, T. Cheong, and G. S. May, "Fault detection and classification in plasma etch equipment for semiconductor manufacturing e-diagnostics," IEEE Transactions on Semiconductor Manufacturing, vol. 25, no. 1, pp. 83-93, 2011.
[36] K. B. Lee, S. Cheon, and C. O. Kim, "A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes," IEEE Transactions on Semiconductor Manufacturing, vol. 30, no. 2, pp. 135-142, 2017.
[37] H. Xu, J. Zhang, Y. Lv, and P. Zheng, "Hybrid feature selection for wafer acceptance test parameters in semiconductor manufacturing," IEEE Access, vol. 8, pp. 17320-17330, 2020.
[38] A. S. Eesa, Z. Orman, and A. M. A. Brifcani, "A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems," Expert systems with applications, vol. 42, no. 5, pp. 2670-2679, 2015.
[39] J. Li et al., "Feature selection: A data perspective," ACM computing surveys (CSUR), vol. 50, no. 6, pp. 1-45, 2017.
[40] A. Jović, K. Brkić, and N. Bogunović, "A review of feature selection methods with applications," in 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO), 2015: Ieee, pp. 1200-1205.
[41] M. Dash and H. Liu, "Feature selection for classification," Intelligent data analysis, vol. 1, no. 1-4, pp. 131-156, 1997.
[42] K. Kira and L. A. Rendell, "The feature selection problem: Traditional methods and a new algorithm," in Aaai, 1992, vol. 2, no. 1992a, pp. 129-134.
[43] I. Kononenko, E. Šimec, and M. Robnik-Šikonja, "Overcoming the myopia of inductive learning algorithms with RELIEFF," Applied Intelligence, vol. 7, no. 1, pp. 39-55, 1997.
[44] I. Kononenko, "Estimating attributes: Analysis and extensions of RELIEF," in European conference on machine learning, 1994: Springer, pp. 171-182.
[45] H. Yang and J. Moody, "Data visualization and feature selection: New algorithms for nongaussian data," Advances in neural information processing systems, vol. 12, 1999.
[46] A. G. Karegowda, A. Manjunath, and M. Jayaram, "Comparative study of attribute selection using gain ratio and correlation based feature selection," International Journal of Information Technology and Knowledge Management, vol. 2, no. 2, pp. 271-277, 2010.
[47] B. Azhagusundari and A. S. Thanamani, "Feature selection based on information gain," International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 2, no. 2, pp. 18-21, 2013.
[48] T. A. Alhaj, M. M. Siraj, A. Zainal, H. T. Elshoush, and F. Elhaj, "Feature selection using information gain for improved structural-based alert correlation," PloS one, vol. 11, no. 11, p. e0166017, 2016.
[49] S. Jadhav, H. He, and K. Jenkins, "Information gain directed genetic algorithm wrapper feature selection for credit rating," Applied Soft Computing, vol. 69, pp. 541-553, 2018.
[50] C. Xiao, J. Ye, R. M. Esteves, and C. Rong, "Using Spearman's correlation coefficients for exploratory data analysis on big dataset," Concurrency and Computation: Practice and Experience, vol. 28, no. 14, pp. 3866-3878, 2016.
[51] Y. Zhang et al., "Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning," Frontiers in computational neuroscience, vol. 9, p. 66, 2015.
[52] X. Jin, A. Xu, R. Bie, and P. Guo, "Machine learning techniques and chi-square feature selection for cancer classification using SAGE gene expression profiles," in International workshop on data mining for biomedical applications, 2006: Springer, pp. 106-115.
[53] H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy," IEEE Transactions on pattern analysis and machine intelligence, vol. 27, no. 8, pp. 1226-1238, 2005.
[54] E. Amaldi and V. Kann, "On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems," Theoretical Computer Science, vol. 209, no. 1-2, pp. 237-260, 1998.
[55] R. Kohavi and G. H. John, "Wrappers for feature subset selection," Artificial intelligence, vol. 97, no. 1-2, pp. 273-324, 1997.
[56] A. Nazir and R. A. Khan, "A novel combinatorial optimization based feature selection method for network intrusion detection," Computers & Security, vol. 102, p. 102164, 2021.
[57] O. Soufan, D. Kleftogiannis, P. Kalnis, and V. B. Bajic, "DWFS: a wrapper feature selection tool based on a parallel genetic algorithm," PloS one, vol. 10, no. 2, p. e0117988, 2015.
[58] S. M. Vieira, L. F. Mendonça, G. J. Farinha, and J. M. Sousa, "Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients," Applied Soft Computing, vol. 13, no. 8, pp. 3494-3504, 2013.
[59] P. Shunmugapriya and S. Kanmani, "A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid)," Swarm and Evolutionary Computation, vol. 36, pp. 27-36, 2017.
[60] A. Wang, N. An, G. Chen, L. Li, and G. Alterovitz, "Accelerating wrapper-based feature selection with K-nearest-neighbor," Knowledge-Based Systems, vol. 83, pp. 81-91, 2015.
[61] L.-Y. Chuang, C.-H. Yang, and C.-H. Yang, "Tabu search and binary particle swarm optimization for feature selection using microarray data," Journal of computational biology, vol. 16, no. 12, pp. 1689-1703, 2009.
[62] Y. Zhang, S. Wang, P. Phillips, and G. Ji, "Binary PSO with mutation operator for feature selection using decision tree applied to spam detection," Knowledge-Based Systems, vol. 64, pp. 22-31, 2014.
[63] M. M. Kabir, M. M. Islam, and K. Murase, "A new wrapper feature selection approach using neural network," Neurocomputing, vol. 73, no. 16-18, pp. 3273-3283, 2010.
[64] S. Maldonado and R. Weber, "A wrapper method for feature selection using support vector machines," Information Sciences, vol. 179, no. 13, pp. 2208-2217, 2009.
[65] J. Cai, J. Luo, S. Wang, and S. Yang, "Feature selection in machine learning: A new perspective," Neurocomputing, vol. 300, pp. 70-79, 2018.
[66] I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Machine learning, vol. 46, no. 1, pp. 389-422, 2002.
[67] S. Dey Sarkar, S. Goswami, A. Agarwal, and J. Aktar, "A novel feature selection technique for text classification using Naive Bayes," International scholarly research notices, vol. 2014, 2014.
[68] H. Bostani and M. Sheikhan, "Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems," Soft computing, vol. 21, no. 9, pp. 2307-2324, 2017.
[69] J. Zhang, Y. Xiong, and S. Min, "A new hybrid filter/wrapper algorithm for feature selection in classification," Analytica chimica acta, vol. 1080, pp. 43-54, 2019.
[70] I. El Naqa and M. J. Murphy, "What is machine learning?," in machine learning in radiation oncology: Springer, 2015, pp. 3-11.
[71] Z. Zhou, C. Wen, and C. Yang, "Fault detection using random projections and k-nearest neighbor rule for semiconductor manufacturing processes," IEEE Transactions on Semiconductor Manufacturing, vol. 28, no. 1, pp. 70-79, 2014.
[72] K. G. Ranjan, B. R. Prusty, and D. Jena, "Review of preprocessing methods for univariate volatile time-series in power system applications," Electric power systems research, vol. 191, p. 106885, 2021.
[73] B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," in Proceedings of the fifth annual workshop on Computational learning theory, 1992, pp. 144-152.
[74] M. Awad, R. Khanna, M. Awad, and R. Khanna, "Support vector machines for classification," Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, pp. 39-66, 2015.
[75] C. Cortes and V. Vapnik, "Support-vector networks," Machine learning, vol. 20, pp. 273-297, 1995.
[76] J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, "A comprehensive survey on support vector machine classification: Applications, challenges and trends," Neurocomputing, vol. 408, pp. 189-215, 2020.
[77] L. Breiman, "Random forests," Machine learning, vol. 45, no. 1, pp. 5-32, 2001.
[78] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Cart," Classification and Regression Trees, 1984.
[79] 許. 簡禎富, 大數據分析與資料挖礦. 前程文化, 2018.
[80] Z. Beheshti and S. M. H. Shamsuddin, "A review of population-based meta-heuristic algorithms," Int. J. Adv. Soft Comput. Appl, vol. 5, no. 1, pp. 1-35, 2013.
[81] W.-C. Yeh, "A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems," Expert Systems with Applications, vol. 36, no. 5, pp. 9192-9200, 2009.
[82] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN'95-international conference on neural networks, 1995, vol. 4: IEEE, pp. 1942-1948.
[83] W.-C. Yeh, "Orthogonal simplified swarm optimization for the series–parallel redundancy allocation problem with a mix of components," Knowledge-Based Systems, vol. 64, pp. 1-12, 2014.
[84] C.-L. Huang, "A particle-based simplified swarm optimization algorithm for reliability redundancy allocation problems," Reliability Engineering & System Safety, vol. 142, pp. 221-230, 2015.
[85] J.-H. Lee, W.-C. Yeh, and M.-C. Chuang, "Web page classification based on a simplified swarm optimization," Applied Mathematics and Computation, vol. 270, pp. 13-24, 2015.
[86] P. Lin, S. Cheng, W. Yeh, Z. Chen, and L. Wu, "Parameters extraction of solar cell models using a modified simplified swarm optimization algorithm," Solar Energy, vol. 144, pp. 594-603, 2017.
[87] Y. K. Ever, "Using simplified swarm optimization on path planning for intelligent mobile robot," Procedia computer science, vol. 120, pp. 83-90, 2017.
[88] W.-C. Yeh and S.-Y. Tan, "Simplified swarm optimization for the heterogeneous fleet vehicle routing problem with time-varying continuous speed function," Electronics, vol. 10, no. 15, p. 1775, 2021.
[89] C.-M. Lai, W.-C. Yeh, and Y.-C. Huang, "Entropic simplified swarm optimization for the task assignment problem," Applied Soft Computing, vol. 58, pp. 115-127, 2017.
[90] C.-M. Lai, "Integrating simplified swarm optimization with AHP for solving capacitated military logistic depot location problem," Applied Soft Computing, vol. 78, pp. 1-12, 2019.
[91] W.-C. Yeh, Y.-P. Lin, Y.-C. Liang, C.-M. Lai, and C.-L. Huang, "Simplified swarm optimization for hyperparameters of convolutional neural networks," Computers & Industrial Engineering, vol. 177, p. 109076, 2023.
[92] Y. Y. Chung and N. Wahid, "A hybrid network intrusion detection system using simplified swarm optimization (SSO)," Applied soft computing, vol. 12, no. 9, pp. 3014-3022, 2012.
[93] C.-M. Lai, W.-C. Yeh, and C.-Y. Chang, "Gene Selection using Information Gain and Improved Simplified Swarm Optimization," Neurocomputing, vol. 218, pp. 331-338, 2016.
[94] C.-M. Lai, "Multi-objective simplified swarm optimization with weighting scheme for gene selection," Applied Soft Computing, vol. 65, pp. 58-68, 2018.
[95] Y. Sun, A. K. Wong, and M. S. Kamel, "Classification of imbalanced data: A review," International journal of pattern recognition and artificial intelligence, vol. 23, no. 04, pp. 687-719, 2009.
[96] P. Garcia-Teodoro, J. Diaz-Verdejo, G. Maciá-Fernández, and E. Vázquez, "Anomaly-based network intrusion detection: Techniques, systems and challenges," computers & security, vol. 28, no. 1-2, pp. 18-28, 2009.
[97] Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad, "Network intrusion detection system: A systematic study of machine learning and deep learning approaches," Transactions on Emerging Telecommunications Technologies, vol. 32, no. 1, p. e4150, 2021.
[98] K. G. Al-Hashedi and P. Magalingam, "Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019," Computer Science Review, vol. 40, p. 100402, 2021.
[99] X. Guo, Y. Yin, C. Dong, G. Yang, and G. Zhou, "On the class imbalance problem," in 2008 Fourth international conference on natural computation, 2008, vol. 4: IEEE, pp. 192-201.
[100] F. Thabtah, S. Hammoud, F. Kamalov, and A. Gonsalves, "Data imbalance in classification: Experimental evaluation," Information Sciences, vol. 513, pp. 429-441, 2020.
[101] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of artificial intelligence research, vol. 16, pp. 321-357, 2002.
[102] D. Chicco and G. Jurman, "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation," BMC Genomics, vol. 21, no. 1, p. 6, 2020/01/02 2020, doi: 10.1186/s12864-019-6413-7.
[103] B. C. Ross, "Mutual information between discrete and continuous data sets," PloS one, vol. 9, no. 2, p. e87357, 2014.
[104] L. Brezočnik, I. Fister Jr, and V. Podgorelec, "Swarm intelligence algorithms for feature selection: a review," Applied Sciences, vol. 8, no. 9, p. 1521, 2018.
[105] R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in Ijcai, 1995, vol. 14, no. 2: Montreal, Canada, pp. 1137-1145.
[106] J. Gorodkin, "Comparing two K-category assignments by a K-category correlation coefficient," Computational biology and chemistry, vol. 28, no. 5-6, pp. 367-374, 2004.
[107] G. Jurman, S. Riccadonna, and C. Furlanello, "A comparison of MCC and CEN error measures in multi-class prediction," 2012.
[108] D. Chicco and G. Jurman, "A statistical comparison between Matthews correlation coefficient (MCC), prevalence threshold, and Fowlkes–Mallows index," Journal of Biomedical Informatics, p. 104426, 2023.
[109] C. L. Nutt et al., "Gene expression-based classification of malignant gliomas correlates better with survival than histological classification," Cancer research, vol. 63, no. 7, pp. 1602-1607, 2003.
[110] Z. Zhu, Y.-S. Ong, and M. Dash, "Markov blanket-embedded genetic algorithm for gene selection," Pattern Recognition, vol. 40, no. 11, pp. 3236-3248, 2007.
[111] U. Alon et al., "Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays," Proceedings of the National Academy of Sciences, vol. 96, no. 12, pp. 6745-6750, 1999.
[112] E. F. Petricoin et al., "Use of proteomic patterns in serum to identify ovarian cancer," The lancet, vol. 359, no. 9306, pp. 572-577, 2002.
[113] A. Dabba, A. Tari, S. Meftali, and R. Mokhtari, "Gene selection and classification of microarray data method based on mutual information and moth flame algorithm," Expert Systems with Applications, vol. 166, p. 114012, 2021.
[114] M. K. Heris, "Practical Genetic Algorithms in Python and MATLAB – Video Tutorial," Yarpiz, 2020. [Online]. Available: https://yarpiz.com/632/ypga191215-practical-genetic-algorithms-in-python-and-matlab.
[115] X.-F. Song, Y. Zhang, Y.-N. Guo, X.-Y. Sun, and Y.-L. Wang, "Variable-size cooperative coevolutionary particle swarm optimization for feature selection on high-dimensional data," IEEE Transactions on Evolutionary Computation, vol. 24, no. 5, pp. 882-895, 2020.
[116] K. Chen, B. Xue, M. Zhang, and F. Zhou, "Evolutionary multitasking for feature selection in high-dimensional classification via particle swarm optimization," IEEE Transactions on Evolutionary Computation, vol. 26, no. 3, pp. 446-460, 2021.
[117] A. Chaudhuri and T. P. Sahu, "A hybrid feature selection method based on Binary Jaya algorithm for micro-array data classification," Computers & Electrical Engineering, vol. 90, p. 106963, 2021.
[118] S. K. Baliarsingh, K. Muhammad, and S. Bakshi, "SARA: a memetic algorithm for high-dimensional biomedical data," Applied Soft Computing, vol. 101, p. 107009, 2021.