[1] | Abonyi, J., & Szeifert, F. (2003). Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognition Letters, 24(14), 2195-2207. |
[2] | Aksu D., Üstebay S., Aydin M.A., Atmaca T. (2018) Intrusion Detection with Comparative Analysis of Supervised Learning Techniques and Fisher Score Feature Selection Algorithm. In: Czachórski T., Gelenbe E., Grochla K., Lent R. (eds) Computer and Information Sciences. ISCIS 2018. Communications in Computer and Information Science, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-00840-6_16. |
[3] | Bagui, S., Bagui, S., Pal, K., & Pal, N. R. (2003). Breast cancer detection using rank nearest neighbor classification rules. Pattern Recognition, 36, 25-34. |
[4] | Bagui, S., Nandi, D., Bagui, S. and White, R. J. (2019). Classifying Phishing Email Using Machine Learning and Deep Learning. Proceedings of the 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), June 3-4, Oxford, England, 439-440. DOI: 10.1109/CyberSecPODS.2019.8885143. Publisher: IEEE. |
[5] | Bagui, S., Shah, K., Hu, Y., & Bagui, S. (2021). Binary Classification of Network-Generated Flow Data Using a Machine Learning Algorithm. International Journal of Information Security and Privacy (IJISP), 15(1), 26-43. DOI: 10.4018/IJISP.2021010102. |
[6] | Bagui, S., & Woods, T. (2021). Machine Learning for Android Ransomware Detection. International Journal of Computer Science and Information Security, 19(1), 29-38. |
[7] | Biswas, N., Chakraborty, S., Mullick, S. S., & Das, S. (2018). A parameter independent fuzzy weighted k-nearest neighbor classifier. Pattern Recognition Letters, 101, 80-87. |
[8] | Farid, D. M., & Rahman, C. M. (2013). Assigning Weights to Training Instances Increases Classification Accuracy. International Journal of Data Mining & Knowledge Management Process (IJDKP), 3(1), 13-25. DOI: 10.5121/ijdkp.2013.3102. |
[9] | Gajowniczek, K., Grzegorczyk, I., Zabkowshi, T., Bajaj, C. (2020). Weighted Random Forests to Improve Arrhythmia Classification, Electronics, 9(99). doi: 10.3390/electronics9010099. |
[10] | Gupta, M. (2012). Dynamic k-NN with Attribute Weighting for Automatic Web Page Classification (Dk-NNwAW). International Journal of Computer Applications 58(10,34-40. Doi: 10.5120/9321-3554. |
[11] | Hechenbichler, K., & Schliep, K. (2004). Weighted k-Nearest-Neighbor Techniques and Ordinal Classification. Sonderforschungsbereich 386, Paper 399. https://epub.ub.uni-muenchen.de/. |
[12] | Jain, V.K., Sharma, J., Singhal, K., Phophalia, A. (2019). Exponentially Weighted Random Forest, Pattern Recognition and Machine Intelligence, 8th International Conference, PreMI, Tezpur, India, December 17-20. |
[13] | Karabatak, M. (2015. A new classifier for breast cancer detection based on Naive Bayesian. Measurement, 72, 32-36. |
[14] | Ma, L., Ofoghi, B., Watters, P., and Brown, S., (2009). Detecting phishing emails using hybrid features, Proceedings of the Symposia and Workshops on Ubiquitous, Automatic and Trusted Computing, 493-497. DOI 10.1109/UIC-ATC.2009.103. |
[15] | Ma, C., Du, X., Cao, L. (2020). Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow, Electronics, 9(20), 324. https://doi.org/10.3390/electronics9020324. |
[16] | Mangasarian, O. L., & Wolberg, W. H. (1990). Cancer diagnosis via linear programming. SIAM News, 23 (5), 1-18. |
[17] | Polo, J. L., Berzal, F., & Cubero, J. C. Weighted classification using decision tree for binary classification problems. Lecture Notes in Computer Science, 4413. |
[18] | Quinlan, J. (1996). Improved use of continuous attributes in C 4.5. Journal of Artificial Intelligence Research, 4, 77-90. |
[19] | Shah, C. (2020). A Hands-On Introduction to Data Science. Cambridge University Press, United Kingdom. |
[20] | Shahhosseini, M. and Hu, G. (2021). Improved Weighted Random Forest for Classification Problems, Intelligent Decision Science, 42-56. DOI: 10.1007/978-3-030-66501-2_4. |
[21] | Sheikhi, S., Kheirabadi, M. T., Bazzazi, A. (2020). A Novel Scheme for Improving Accuracy of KNN Classification Algorithm Based on the New Weighting Technique and Stepwise Feature Selection, Journal of Information Technology Management, 12(4), 90-104. |
[22] | Singer, G., Anuar, R., Ben-Gal, I. (2020). A weighted information-gain measure for ordinal classification trees, Expert Systems With Applications, 152, 113375. |
[23] | Suruliandi, A., David, H. B. F., Raja, S. P. (2020). Attribute rank-based weighted decision tree, International Journal of Applied Decision Sciences, 13(1), 46-73. 10.1504/IJADS.2020.104309. |
[24] | Syarif, A. R. and Gata, W (2017). Intrusion detection system using hybrid binary PSO and K-nearest neighborhood algorithm, Proc. 11th Int. Conf. Inf. Commun. Technol. Syst. (ICTS), Oct. 2017, pp. 181–186. |
[25] | Taheri, S., Yearwood, J., Mammadov, M., & Seifollahi, S. (2014). Attribute weighted Naive Bayes classifier using a local optimization. Neural Computing and Applications, 24, 995-1002. Doi: 10.1007/s00521-012-1329-z. |
[26] | UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/index.php. |
[27] | Wettschereck, D., Aha, D. W., & Mohri, T. (1997). A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithm. Artificial Intelligence Review, 11, 273-314. |
[28] | Wolpert, D.H. (1990). Constructing a generalizer superior to NETtalk via a mathematical theory of generalization. Neural Networks 3, 445-452. |
[29] | Yeh, I-C., Yang, K-J., & Ting, T-M. (2008). Knowledge discovery on RFM model using Bernoulli sequence. Expert Systems with Application, 36(3). |
[30] | Wu, J., & Cai, Z. (2011). Attribute weighting via differential evolution algorithm for attribute weighted Naïve Bayes (WNB). Journal of Computational Information Systems, 7(5), 1672-1679. |
[31] | Wu, J., Pan, S., Cai, Z., Zhu, X., & Zhang, C. (2014). Dual instance and attribute weighting for Naive Bayes classification, 2014 International Joint Conference on Neural Networks (IJCNN), 1675-1679. |
[32] | Wu, J., Pan, S., Zhu, X., Cai, Z., Zhang, P., & Zhang, C. (2015). Self-adaptive attribute weighting for Naive Bayes classification. Expert Systems with Applications, 42(3), 1487-1502. https://doi.org/10.1016/j.eswa.2014.09.019. |
[33] | Xiang, Z-L., Yu, X-R., & Kang, D-K. (2015). Bayesian Prediction Model Based on Attribute Weighting and Kernel Density Estimations. Mathematical Problems in Engineering, doi.org/10.1155/2015/170324. |
[34] | Xuan S., Liu G., Li Z. (2018) Refined Weighted Random Forest and Its Application to Credit Card Fraud Detection. In: Chen X., Sen A., Li W., Thai M. (eds) Computational Data and Social Networks. CSoNet 2018. Lecture Notes in Computer Science, vol 11280. Springer, Cham. https://doi.org/10.1007/978-3-030-04648-4_29. |
[35] | Yao, S., & Li, L. (2012). Weighted Naïve Bayes Classification Algorithm Based on Correlation Coefficients. International Journal of Advancements in Computing Technology (IJACT), 4(20). doi: 10.4156/ijact.vol4.issue20.4. |
[36] | Zaidi, N. A., Cerquides, J., Carman, M. J., & Webb, G. I. (2013). Alleviating Naive Bayes Attribute Independence Assumption by Attribute Weighting. Journal of Machine Learning Research, 14, 1947-1988. |
[37] | Zhang, C., & Wang, J. (2010). Attribute weighted Naive Bayesian classification algorithm. 5th International Conference on Computer Science & Education, 27-30. |