Pattern Recognition using Multiclass Support Vector Machine Method with Local Binary Pattern as Feature Extraction
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Keywords:
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Pattern Recognition, Local Binary Pattern, Support Vector Machine, Multiclass SVM
Abstract
Corn is an essential agricultural commodity since it is used in animal feed, biofuel, industrial processing, and the manufacture of non-food industrial commodities such as starch, acid, and alcohol. Early detection of diseases and pests of corn aims to reduce the possibility of crop failure and maintain the quality and quantity of crop yields. A decision tree is a nonparametric classification model in statistical machine learning that predicts target variables using tree-structured decisions. The performance of this model can increase significantly if the continuous predictor variables are discretized into valid categories. However, in some cases, the result does not provide satisfactory performance. The possible cause is the ambiguity in discretizing predictor variables. The incorporation of fuzzy membership functions into the model to resolve discretization ambiguity issues. This work aims to classify diseases and pests of corn plants using the decision tree model and improve the model’s performance by implementing fuzzy membership functions. The main contribution of this work is that we have shown a significant improvement in the decision tree model performance by implementing fuzzy membership functions; S-growth, triangle, and S-shrinkage curves. The proposed fuzzy model is better than the decision tree model, with an average performance increase from the largest to the smallest; kappa (12.16%), recall (11.8%), F-score (9.71%), precision (5.08%), accuracy (3.23%), specificity (1.94%), and AUC (0.49%). The combination of bias and variance generated by the proposed model is quite small, indicating that the model is able to capture data trends well.
References
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Pramudita, D. A. and A. Musdholifah (2020). GSA to Obtain SVM Kernel Parameter for Thyroid Nodule Classication. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 14(1); 11–22
Prashanth, D. S., R. V. K. Mehta, and N. Sharma (2020). Classification of Handwritten Devanagari Number-an Analysis of Pattern Recognition Tool using Neural Network and CNN. Procedia Computer Science, 167; 2445–2457
Sharma, A., A. Mittal, S. Singh, and V. Awatramani (2020). Hand Gesture Recognition using Image Processing and Feature Extraction Techniques. Procedia Computer Science, 173; 181–190
Shiyam, A. L. (2019). Presensi Mahasiswa Dengan Ekstraksi Fitur Wajah Menggunakan Metode Local Binary Pattern. Universitas Nusantara PGRI Kediri (in Indonesia)
Styawati, S. and K. Mustofa (2019). A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 13(3); 219–230
Susanto, A., D. Sinaga, C. A. Sari, E. H. Rachmawanto, and D. Setiadi (2018). A High Performace of Local Binary Pattern on Classify Javanese Character Classification. Scientific Journal of Informatics, 5(1); 8
Tang, L., X. Lu, C. Yang, and X. Li (2019). Extension Support Vector Machine for Extension Classification. Procedia Computer Science, 162; 174–182
Theodoridis, S. (2009). Pattern Recognition. Elsevier
Utami, N. A., W. Maharani, and I. Atastina (2021). Personality Classification of Facebook users According to Big Five Personality using SVM (Support Vector Machine) Method. Procedia Computer Science, 179; 177–184
Vidya, B. S. and E. Chandra (2019). Entropy based Local Binary Pattern (ELBP) Feature Extraction Technique of Multimodal Biometrics as Defence Mechanism for Cloud Storage. Alexandria Engineering Journal, 58(1); 103–114
Wang, D. and Y. Zhao (2020). Using News to Predict Investor Sentiment: Based on SVM Model. Procedia Computer Science, 174; 191–199
Yin, S. and J. Yin (2016). Tuning Kernel Parameters for SVM based on Expected Square Distance Ratio. Information Sciences, 370; 92–102
Agbele, T., B. Ojeme, and R. Jiang (2019). Application of Local Binary Patterns and Cascade AdaBoost Classifier for Mice Behavioural Patterns Detection and Analysis. Procedia Computer Science, 159; 1375–1386
Ali, M. M., P. L. Yannawar, and A. Gaikwad (2017). Multi-Algorithm of Palmprint Recognition System based on Fusion of Local Binary Pattern and Two-Dimensional Locality Preserving Projection. Procedia Computer Science, 115; 482-492
Alita, D., Y. Fernando, and H. Sulistiani (2020). Implementasi Algoritma Multiclass SVM pada Opini Publik Berbahasa Indonesia di Twitter. Jurnal Tekno Kompak, 14(2); 86–91 (in Indonesia)
Amat, R., J. Y. Sari, and I. P. Ningrum (2017). Implementasi Metode Local Binary Patterns untuk Pengenalan Pola Huruf Hiragana dan Katakana pada Smartphone. JUTI: Jurnal Ilmiah Teknologi Informasi, 15(2); 152 (in Indonesia)
Ay, M., D. Stenger, M. Schwenzer, D. Abel, and T. Bergs (2019). Kernel Selection for Support Vector Machines for System Identification of a CNC Machining Center. IFAC-PapersOnLine, 52(29); 192–198
Battineni, G., N. Chintalapudi, and F. Amenta (2019). Machine Learning in Medicine: Performance Calculation of Dementia Prediction by Support Vector Machines (SVM). Informatics in Medicine Unlocked, 16; 100200
Bora, M. B., D. Daimary, K. Amitab, and D. Kandar (2020). Handwritten Character Recognition from Images using CNN-ECOC. Procedia Computer Science, 167; 2403–2409
Caraka, B., B. A. A. Sumbodo, and I. Candradewi (2017). Klasifikasi Sel Darah Putih Menggunakan Metode Support Vector Machine (SVM) Berbasis Pengolahan Citra Digital. IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), 7(1); 25–36 (in Indonesia)
Cervantes, J., F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez (2020). A Comprehensive Survey on Support Vector Machine Classification: Applications, Challenges and Trends. Neurocomputing, 408; 189–215
Chamasemani, F. F. and Y. P. Singh (2011). Multi-Class Support Vector Machine (SVM) Classifiers-an Application in Hypothyroid Detection and Classification. In 2011 6th International Conference on Bio-Inspired Computing: Theories and Applications. Institute of Electrical and Electronics Engineers; 351--356
Galshetwar, G. M., L. M. Waghmare, A. B. Gonde, and S. Murala (2017). Edgy Salient Local Binary Patterns in Inter-Plane Relationship for Image Retrieval in Diabetic Retinopathy. Procedia Computer Science, 115; 440–447
Gonzalez, R. C. (2009). Digital Image Processing. Pearson Education India
Jerlin Rubini, L. and E. Perumal (2020). Eficient Classification of Chronic Kidney Disease by using Multi-Kernel Support Vector Machine and Fruit Fly Optimization Algorithm. International Journal of Imaging Systems and Technology, 30(3); 660–673
Kaewchote, J., S. Janyong, and W. Limprasert (2018). Image Recognition Method using Local Binary Pattern and The Random Forest Classifier to Count Post Larvae Shrimp. Agriculture and Natural Resources, 52(4); 371–376
Komarudin, S., D. Anggraeni, A. Riski, and A. Hadi (2020). Classification of Genetic Expression in Prostate Cancer using Support Vector Machine Method. In Journal of Physics: Conference Series, 1613; 012032
Kumar, M. A. and M. Gopal (2011). Reduced One-Against-All Method for Multiclass SVM Classification. Expert Systems with Applications, 38(11); 14238–14248
Kwabena, P. M., B. A. Weyori, and A. A. Mighty (2020). Exploring The Performance of LBP-Capsule Networks with K-Means Routing on Complex Images. Journal of King Saud University-Computer and Information Sciences
Li, L. L., X. Zhao, M. L. Tseng, and R. R. Tan (2020). Short-Term Wind Power Forecasting based on Support Vector Machine with Improved Dragonfly Algorithm. Journal of Cleaner Production, 242; 118447
Liu, C., W. Wang, M. Wang, F. Lv, and M. Konan (2017). An Efficient Instance Selection Algorithm to Reconstruct Training Set for Support Vector Machine. Knowledge-Based Systems, 116; 58–73
Muqeet, M. A. and R. S. Holambe (2019). Local Binary Patterns based on Directional Wavelet Transform for Expression and Pose-Invariant Face Recognition. Applied Computing and Informatics, 15(2); 163–171
Nayak, J. S. and M. Indiramma (2021). An Approach to Enhance Age Invariant Face Recognition Performance based on Gender Classification. Journal of King Saud University-Computer and Information Sciences
Nugraha, E., A. Wibawa, M. Hakim, U. Kholifah, R. Dini, and M. Irwanto (2019). Implementation of Fuzzy Tsukamoto Method in Decision Support System of Journal Acceptance. In Journal of Physics: Conference Series, 1280; 022031
Ojala, T., M. Pietikäinen, and T. Mäenpää (2000). Gray Scale and Rotation Invariant Texture Classication with Local Binary Patterns. In European Conference on Computer Vision, 1842; 404–420
Pramudita, D. A. and A. Musdholifah (2020). GSA to Obtain SVM Kernel Parameter for Thyroid Nodule Classication. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 14(1); 11–22
Prashanth, D. S., R. V. K. Mehta, and N. Sharma (2020). Classification of Handwritten Devanagari Number-an Analysis of Pattern Recognition Tool using Neural Network and CNN. Procedia Computer Science, 167; 2445–2457
Sharma, A., A. Mittal, S. Singh, and V. Awatramani (2020). Hand Gesture Recognition using Image Processing and Feature Extraction Techniques. Procedia Computer Science, 173; 181–190
Shiyam, A. L. (2019). Presensi Mahasiswa Dengan Ekstraksi Fitur Wajah Menggunakan Metode Local Binary Pattern. Universitas Nusantara PGRI Kediri (in Indonesia)
Styawati, S. and K. Mustofa (2019). A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 13(3); 219–230
Susanto, A., D. Sinaga, C. A. Sari, E. H. Rachmawanto, and D. Setiadi (2018). A High Performace of Local Binary Pattern on Classify Javanese Character Classification. Scientific Journal of Informatics, 5(1); 8
Tang, L., X. Lu, C. Yang, and X. Li (2019). Extension Support Vector Machine for Extension Classification. Procedia Computer Science, 162; 174–182
Theodoridis, S. (2009). Pattern Recognition. Elsevier
Utami, N. A., W. Maharani, and I. Atastina (2021). Personality Classification of Facebook users According to Big Five Personality using SVM (Support Vector Machine) Method. Procedia Computer Science, 179; 177–184
Vidya, B. S. and E. Chandra (2019). Entropy based Local Binary Pattern (ELBP) Feature Extraction Technique of Multimodal Biometrics as Defence Mechanism for Cloud Storage. Alexandria Engineering Journal, 58(1); 103–114
Wang, D. and Y. Zhao (2020). Using News to Predict Investor Sentiment: Based on SVM Model. Procedia Computer Science, 174; 191–199
Yin, S. and J. Yin (2016). Tuning Kernel Parameters for SVM based on Expected Square Distance Ratio. Information Sciences, 370; 92–102
Authors
Irsalinda, N., Surono, S., & Ratna Sary, I. D. . (2022). Pattern Recognition using Multiclass Support Vector Machine Method with Local Binary Pattern as Feature Extraction. Science and Technology Indonesia, 7(3), 269–274. https://doi.org/10.26554/sti.2022.7.3.269-274
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