FSPL: A meta-learning approach for a filter and embedded feature selection pipeline

• Autorzy: Lazebnik Teddy , Rosenfeld Avi

Identyfikatory

DOI

10.34768/amcs-2023-0009

• Języki publikacji: EN

Abstrakty

EN

There are two main approaches to tackle the challenge of finding the best filter or embedded feature selection (FS) algorithm: searching for the one best FS algorithm and creating an ensemble of all available FS algorithms. However, in practice, these two processes usually occur as part of a larger machine learning pipeline and not separately. We posit that, due to the influence of the filter FS on the embedded FS, one should aim to optimize both of them as a single FS pipeline rather than separately. We propose a meta-learning approach that automatically finds the best filter and embedded FS pipeline for a given dataset called FSPL. We demonstrate the performance of FSPL on n = 90 datasets, obtaining 0.496 accuracy for the optimal FS pipeline, revealing an improvement of up to 5.98 percent in the model’s accuracy compared to the second-best meta-learning method.

Słowa kluczowe

EN

feature selection pipeline; meta learning; no free lunch; autoML; genetic algorithm

PL

wybór funkcji; metauczenie; algorytm genetyczny

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2023
• Tom: Vol. 33, no. 1
• Strony: 103--115
• Opis fizyczny: Bibliogr. 82 poz., rys., tab., wykr.

Twórcy

autor

Lazebnik Teddy

• Department of Cancer Biology, University College London Cancer Institute, 72 Huntley St., WC1E 6DD, London, UK

autor

Rosenfeld Avi

• Department of Computer Science, Jerusalem College of Technology, 21 Ha-Va’ad ha-Le’umi St., Jerusalem, Israel

Bibliografia

• [1] Aarts, E.H.L. and van Laarhoven, P.J.M. (1987). Simulated annealing: A pedestrian review of the theory and some applications, in P.A. Devijver and J. Kittler (Eds), Pattern Recognition Theory and Applications, Springer, Berlin/Heidelberg, pp. 179-192.
• [2] Abdullah, A.S., Selvakumar, S., Karthikeyan, P. and Venkatesh, M. (2017). Comparing the efficacy of decision tree and its variants using medical data, Indian Journal of Science and Technology 10: 1-8.
• [3] Akshaikhdeeb, B. and Ahmad, K. (2017). Feature selection for chemical compound extraction using wrapper approach with naive Bayes classifier, 6th International Conference on Electrical Engineering and Informatics (ICEEI), Langkawi, Malaysia, pp. 1-6.
• [4] Anthony, T., Tian, Z. and Barber, D. (2017). Thinking fast and slow with deep learning and tree search, Conference on Neural Information Processing Systems, Long Beach, USA.
• [5] Azhagusundari, B. and Thanamani, A.S. (2013). Feature selection based on information gain, International Journal of Innovative Technology and Exploring Engineering 2(2): 18-21.
• [6] Bilalli, B., Abelló, A. and Aluja-Banet, T. (2017). On the predictive power of metafeatures in OpenML, International Journal of Applied Mathematics and Computer Science 27(4): 697-712, DOI: 10.1515/amcs-2017-0048.
• [7] Bo, L. and Rein, L. (2005). Comparison of the Luus-Jaakola optimization procedure and the genetic algorithm, Engineering Optimization 37(4): 381-396.
• [8] Bo, Z.W., Hua, L.Z. and Yu, Z.G. (2006). Optimization of process route by genetic algorithms, Robotics and Computer-Integrated Manufacturing 22: 180-188.
• [9] Bolón-Canedo, V. and Alonso-Betanzos, A. (2019). Ensembles for feature selection: A review and future trends, Information Fusion 52: 1-12.
• [10] Brazdil, P., Giraud-Carrier, C., Soares, C. and Vilalta, R. (2009). Metalearning: Applications to Data Minings, Springer, Berlin/Heidelberg.
• [11] Chandrashekar, G. and Sahin, F. (2014). A survey on feature selection methods, Computers & Electrical Engineering 40(1): 16-28.
• [12] Chengzhang, L. and Jiucheng, X. (2019). Feature selection with the Fisher score followed by the maximal clique centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma, Scientific Reports 9: 17283.
• [13] Drori, I., Krishnamurthy, Y., Rampin, R., de Paula Lourenco, R., Ono, J.P., Cho, K., Silva, C. and Freire, J. (2018). AlphaD3M: Machine learning pipeline synthesis, AutoML Workshop at ICML, Stockholm, Sweden.
• [14] Engels, R. and Theusinger, C. (1998). Using a data metric for preprocessing advice for data mining applications, European Conference on Artificial Intelligence, Brighton, UK, pp. 23-28.
• [15] Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M. and Smola, A. (2020). AutoGluon-tabular: Robust and accurate AutoML for structured data, arXiv: 2003.06505.
• [16] Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M. and Hutter, F. (2020). Auto-Sklearn 2.0: Hands-free AutoML via meta-learning, arXiv: 2007.04074.
• [17] Feurer, M., Klevin, A., Eggensperger, K., Springenberg, J.T., Blum, M. and Hutter, F. (2019). Auto-sklearn: Efficient and robust automated machine learning, in F. Hutter et al. (Eds), Automated Machine Learning, Springer, Cham, pp. 113-134.
• [18] Feurer, M., Springenberg, J.T. and Hutter, F. (2014). Using meta-learning to initialize Bayesian optimization of hyperparameters, International Conference on Metalearning and Algorithm Selection, Prague, Czech Republic, pp. 3-10.
• [19] Feurer, M., Springenberg, J.T. and Hutter, F. (2015). Initializing Bayesian hyperparameter optimization via meta-learning, Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin, USA, pp. 1128-1135.
• [20] Freitas, A.A. (2014). Comprehensible classification models: A position paper, ACM SIGKDD Explorations Newsletter 15(1): 1-10.
• [21] Fushiki, T. (2011). Estimation of prediction error by using k-fold cross-validation, Statistical Computation 21: 137-146.
• [22] Ghaheri, A., Shoar, S., Naderan, M. and Hoseini, S.S. (2005). The applications of genetic algorithms in medicine, Oman Medical Journal 30(6): 406-416.
• [23] Gil, Y., Yao, K.-T., Ratnakar, V., Garijo, D., Steeg, G.V., Szekely, P., Brekelmans, R., Kejriwal, M., Lau, F. and Huang, I.-H. (2018). P4ml: A phased performance-based pipeline planner for automated machine learning, AutoML Workshop at ICML, Stockholm, Sweden.
• [24] Grabmeier, J.L. and Lambe, L.A. (2007). Decision trees for binary classification variables grow equally with the Gini impurity measure and Pearson’s chi-square test, International Journal of Business Intelligence and Data Mining 2(2): 213-226.
• [25] Gu, Q., Li, Z. and Han, J. (2011). Generalized Fisher score for feature selection, Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, Barcelona, Spain, p. 266-273.
• [26] He, X., Zhao, K. and Chu, X. (2021). AutoML: A survey of the state-of-the-art, Knowledge-Based Systems 212: 106622.
• [27] Holland, J.H. (1992). Genetic algorithms, Scientific American 267(1): 66-73.
• [28] Ivosev, G., Burton, L. and Bonner, R. (2008). Dimensionality reduction and visualization in principal component analysis, Analytical Chemistry 80(13): 4933-4944.
• [29] Kang, Y., Cai, Z., Tan, C.-W., Huang, Q. and Liu, H. (2020). Natural language processing (NLP) in management research: A literature review, Journal of Management Analytics 7(2): 139-172.
• [30] Kanna, S.S. and Ramaraj, N. (2010). Feature selection algorithms: A survey and experimental evaluation, Knowledge-Based Systems 23(6): 580-585.
• [31] Keren Simon, L., Liberzon, A. and Lazebnik, T. (2023). A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge, Scientific Reports 13(1): 1249.
• [32] Kietz, J.-U., Serban, F., Bernstein, A. and Fischer, S. (2012). Designing KDD workflows via HTN-planning for intelligent discovery assistance, 5th Planning to Learn Workshop at the European Conference on Artificial Intelligence, Montpellier, France.
• [33] Kumar, V. and Minz, S. (2014). Feature selection: A literature review, Smart Computing Review 4(3): 211-229.
• [34] Kusy, M. and Zajdel, R. (2021). A weighted wrapper approach to feature selection, International Journal of Applied Mathematics and Computer Science 31(4): 685-696, DOI: 10.34768/amcs-2021-0047.
• [35] Lazebnik, T., Zaher, B., Bunimovich-Mendrazitsky, S. and Halachmi, S. (2022). Predicting acute kidney injury following open partial nephrectomy treatment using sat-pruned explainable machine learning model, BMC Medical Informatics and Decision Making 22: 133.
• [36] Lemka, C., Budka, M. and Gabrys, B. (2015). Metalearning: A survey of trends and technologies, Artificial Intelligence Review 44(1): 117-130.
• [37] Lin, X., Li, C., Ren, W., Luo, X. and Qi, Y. (2019). A new feature selection method based on symmetrical uncertainty and interaction gain, Computational Biology and Chemistry 83: 107149.
• [38] Liu, Y., Mu, Y., Chen, K., Li, Y. and Guo, J. (2020). Daily activity feature selection in smart homes based on Pearson correlation coefcient, Neural Processing Letters 51: 1771-1787.
• [39] Luo, G. (2016). A review of automatic selection methods for machine learning algorithms and hyper-parameter values, Network Modeling Analysis in Health Informatics and Bioinformatics 5(1): 18.
• [40] Ma, L., Li, M., Gao, Y., Chen, T., Ma, X. and Qu, L. (2017). A novel wrapper approach for feature selection in object-based image classification using polygon-based cross-validation, IEEE Geoscience and Remote Sensing Letters 14(3): 409-413.
• [41] Maile, H., Li, J.O., Gore, D., Leucci, M., Mulholland, P., Hau, S., Szabo, A., Moghul, I., Balaskas, K., Fujinami, K., Hysi, P., Davidson, A., Liskova, P. Hardcastle, A., Tuft, S. and Pontikos, N. (2021). Machine learning algorithms to detect subclinical keratoconus: Systematic review, JMIR Medical Informatics 9(12): e27363.
• [42] Molina, L.C., Belanche, L. and Nebot, A. (2002). Feature selection algorithms: A survey and experimental evaluation, 2002 IEEE International Conference on Data Mining, Maebashi City, Japan, pp. 306-313.
• [43] Mussa, D.J. and Jameel, N. G.M. (2019). Relevant SMS spam feature selection using wrapper approach and XGBoost algorithm, Kurdistan Journal of Applied Research 4(2): 110-120.
• [44] Muthukrishnan, R. and Rohini, R. (2016). Lasso: A feature selection technique in predictive modeling for machine learning, IEEE International Conference on Advances in Computer Applications (ICACA), Coimbatore, India, pp. 18-20.
• [45] Neumann, J., Schnorr, C. and Steidl, G. (2005). Combined SVM-based feature selection and classification, Machine Learning 61: 129-150.
• [46] Nguyen, P., Hilario, M. and Kalousis, A. (2014). Using meta-mining to support data mining workflow planning and optimization, Journal of Artificial Intelligence Research 51: 605-644.
• [47] Nisioti, E., Chatzidimitriou, K.C. and Symeonidis, A.L. (2018). Predicting hyperparameters from meta-features in binary classification problems, AutoML Workshop at International Conference on Machine Learning, Stockholm, Sweden.
• [48] Oliveto, P. S. and Witt, C. (2015). Improved time complexity analysis of the simple genetic algorithm, Theoretical Computer Science 605: 21-41.
• [49] Olson, R.S. and Moore, J.H. (2016). TPOT: A tree-based pipeline optimization tool for automating machine learning, JMLR: Workshop and Conference Proceedings 64: 66-74.
• [50] Ometto, G., Moghul, I., Montesano, G., Hunter, A., Pontikos, N., Jones, P. R., Keane, P.A., Liu, X., Denniston, A.K. and Crabb, D.P. (2019). ReLayer: A free, online tool for extracting retinal thickness from cross-platform oct images, Translational Vision Science and Technology 8(3): 25.
• [51] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. (2011). Scikit-learn: Machine learning in Python, Journal of Machine Learning Research 12: 2825-2830.
• [52] Pinto, F., Cerqueira, V., Soares, C. and Mendes-Moreira, J. (2017). Autobagging: Learning to rank bagging workflows with metalearning, arXiv: 1706.09367.
• [53] Plackett, R.L. (1983). Karl Pearson and the chi-squared test, International Statistical Review/Revue Internationale de Statistique 51: 59-72.
• [54] Reif, M., Shafait, F. and Dengel, A. (2012). Meta-learning for evolutionary parameter optimization of classifiers, Machine Learning 87: 357-380.
• [55] Rice, J.R. (1976). The algorithm selection problem, Advances in Computers 15: 65-118.
• [56] Rokach, L. (2016). Decision forest: Twenty years of research, Information Fusion 27: 111-125.
• [57] Rosenfeld, A. (2021). Better metrics for evaluating explainable artificial intelligence, AAMAS’21: 20th International Conference on Autonomous Agents and Multiagent Systems, pp. 45-50, (virtual).
• [58] Rosenfeld, A. and Freiman, M. (2021). Explainable feature ensembles through homogeneous and heterogeneous intersections, JCAI-PRICAI 2020 Workshop on Explainable Artificial Intelligence, (online).
• [59] Rosenfeld, A., Graham, D.G., Hamoudi, R., Butawan, R., Eneh, V., Khan, S., Miah, H., Niranjan, M. and Lovat, L.B. (2015). MIAT: A novel attribute selection approach to better predict upper gastrointestinal cancer, International Conference on Data Science and Advanced Analytics, Paris, France.
• [60] Rosenfeld, A. and Richardson, A. (2019). Explainability in human-agent systems, Autonomous Agents and MultiAgent Systems 33(6): 673-705.
• [61] Saeys, Y., Abeel, T. and de Peer, Y.V. (2008). Robust feature selection using ensemble feature selection techniques, in W. Daelemans et al. (Eds), Machine Learning and Knowledge Discovery in Databases, Springer, Berlin, pp. 313-325.
• [62] Savchenko, E. and Lazebnik, T. (2022). Computer aided functional style identification and correction in modern Russian texts, Journal of Data, Information and Management 4: 25-32.
• [63] Seijo-Pardo, B., Porto-Díaz, I., Bolón-Canedo, V. and Alonso-Betanzos, A. (2017). Ensemble feature selection: Homogeneous and heterogeneous approaches, KnowledgeBased Systems 118: 124-139.
• [64] Serban, F., Vanschoren, J., Kietz, J.U. and Bernstein, A.A. (2013). A survey of intelligent assistants for data analysis, ACM Computing Surveys 45(3): 1-35.
• [65] Sharma, A., Imoto, S. and Miyano, S. (2012). A top-r feature selection algorithm for microarray gene expression data, IEEE/ACM Transactions on Computational Biology and Bioinformatics 9(3): 754-764.
• [66] Shatte, A.B.R., Hutchinson, D.M. and Teague, S.J. (2019). Machine learning in mental health: A scoping review of methods and applications, Psychological Medicine 49(9): 1426-1448.
• [67] Shen, Z., Chen, X. and Garibaldi, J.M. (2020). A novel meta learning framework for feature selection using data synthesis and fuzzy similarity, IEEE World Congress on Computational Intelligence, (online).
• [68] Shilbayeh, S. and Vadera, S. (2014). Feature selection in meta learning framework, Science and Information Conference, London, UK, pp. 269-275.
• [69] Smith-Miles, K.A. (2009). Cross-disciplinary perspectives on meta-learning for algorithm selection, ACM Computational Surveys 41(1): 6.
• [70] Soares, C., Brazdil, P.B. and Kuba, P. (2004). A meta-learning method to select the kernel width in support vector regression, Machine Learning 54: 195-209.
• [71] Strang, B., van der Putten, P., van Rijn, J.N. and Hutter, F. (2018). Don’t rule out simple models prematurely: A large scale benchmark comparing linear and non-linear classifiers in OpenML, in W. Duivesteijn et al. (Eds), Advances in Intelligent Data Analysis XVII, Springer, Berlin, pp. 303-315.
• [72] Swain, P. H. and Hauska, H. (1977). The decision tree classifier: Design and potential, IEEE Transactions on Geoscience Electronics 15(3): 142-147.
• [73] Tang, J., Alelyani, S. and Liu, H. (2014). Feature Selection for Classification: A Review, CRC Press, Boca Raton.
• [74] Teisseyre, P. (2022). Joint feature selection and classification for positive unlabelled multi-label data using weighted penalized empirical risk minimization, International Journal of Applied Mathematics and Computer Science 32(2): 311-322, DOI: 10.34768/amcs-2022-0023.
• [75] Tokarev, K.E., Zotov, V.M., Khavronina, V.N. and Rodionova, O.V. (2021). Convolutional neural network of deep learning in computer vision and image classification problems, IOP Conference Series: Earth and Environmental Science 786(1): 012040.
• [76] Vanschoren, J. (2018). Meta-learning: A survey, arXiv: 1810.03548.
• [77] Vasan, K.K. and Surendiran, B. (2016). Dimensionality reduction using principal component analysis for network intrusion detection, Perspectives in Science 8: 510-512.
• [78] Waring, J., Lindvall, C. and Umeton, R. (2020). Automated machine learning: Review of the state-of-the-art and opportunities for healthcare, Artificial Intelligence in Medicine 104: 101822.
• [79] Wasimuddin, M., Elleithy, K., Abuzneid, A.-S., Faezipour, M. and Abuzaghleh, O. (2020). Stages-based ECG signal analysis from traditional signal processing to machine learning approaches: A survey, IEEE Access 8: 177782-177803.
• [80] Wu, S., Roberts, K., Datta, S., Du, J., Ji, Z., Si, Y., Soni, S., Wang, Q., Wei, Q., Xiang, Y., Zhao, B. and Xu, H. (2020). Deep learning in clinical natural language processing: A methodical review, Journal of the American Medical Informatics Association 27(3): 457-470.
• [81] Zebari, R.R., Abdulazeez, A.M., Zeebaree, D.Q., Zebari, D.A. and Saeed, J.N. (2020). A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction, Journal of Applied Science and Technology Trends 1(2): 56-70.
• [82] Zhu, X., Huang, Z., T., S.H., Cheng, J. and Xu, C. (2012). Dimensionality reduction by mixed kernel canonical correlation analysis, Pattern Recognition 45(8): 3003-3016.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)