Incremental learning of concept drift in Multiple Instance Learning for industrial visual inspection

Histograms of Oriented Gradients for Human Detection

Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works

Pattern Classification

Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,525 · see citing works

Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398 · see citing works

Advances in Neural Information Processing Systems 14

(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 9,184 · see citing works

Pattern Classification

Russell, E. L., Chiang, L. H., & Braatz, R. D. (2000) · Advances in industrial control · doi:10.1007/978-1-4471-0409-4_3 · Cited by 7,938 · see citing works

A survey on concept drift adaptation

Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014) · ACM Computing Surveys · doi:10.1145/2523813 · Cited by 3,735 · see citing works

Ensemble based systems in decision making

Robi Polikar, A. (2006) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2006.1688199 · Cited by 2,960 · see citing works

Solving the multiple instance problem with axis-parallel rectangles

Dietterich, T. G., Lathrop, R. H., & Lozano-Pérez, T. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(96)00034-3 · Cited by 2,866 · see citing works

Digital Image Processing

ROSENTHALER, L. (2007) · Elsevier eBooks · doi:10.1016/b978-0-240-80740-9.50068-4 · Cited by 2,318 · see citing works

Robust object tracking with online multiple instance learning

Babenko, B., Yang, M.-H., & Belongie, S. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.226 · Cited by 2,007 · see citing works

Advances in Neural Information Processing Systems 18

Murray, I., Mackay, D., Zoubin Ghahramani, A., & Skilling, J. (2006) · Cited by 1,927 · see citing works

Learning in the Presence of Concept Drift and Hidden Contexts

Widmer, G., & Kubat, M. (1996) · Machine Learning · doi:10.1023/a:1018046501280 · Cited by 1,857 · see citing works

Mining time-changing data streams

Hulten, G., Spencer, L. A., & Domingos, P. (2001) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502529 · Cited by 1,784 · see citing works

Learning with Drift Detection

da Gama, J. M. P., Medas, P., Castillo, G., & Rodrigues, P. P. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-28645-5_29 · Cited by 1,635 · see citing works

Support Vector Machines for Multiple-Instance Learning

Andrews, S. J., Tsochantaridis, I., & Hofmann, T. F. (2002) · Cited by 1,370 · see citing works

A streaming ensemble algorithm (SEA) for large-scale classification

Street, W. N., & Yongseog Kim, A. (2001) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502568 · Cited by 1,232 · see citing works

A Framework for Multiple-Instance Learning

Maron, O., & Lozano‐Pérez, T. (1997) · Cited by 1,232 · see citing works

Ensemble learning for data stream analysis: A survey

Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., & Woźniak, M. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.004 · Cited by 1,050 · see citing works

Incremental Learning of Concept Drift in Nonstationary Environments

Elwell, R., & Robi Polikar, A. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2160459 · Cited by 928 · see citing works

Learn++: an incremental learning algorithm for supervised neural networks

Polikar, R., Upda, L., Upda, S. S., & Honavar, V. (2001) · IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/5326.983933 · Cited by 888 · see citing works

Learning in Nonstationary Environments: A Survey

Ditzler, G., Roveri, M., Alippi, C., & Robi Polikar, A. (2015) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2015.2471196 · Cited by 799 · see citing works

MILES: Multiple-Instance Learning via Embedded Instance Selection

Chen, Y., Bi, J., & Wang, J. Z. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.248 · Cited by 748 · see citing works

Multiple Instance Boosting for Object Detection

Zhang, C., Platt, J. C., & Viola, P. A. (2005) · neural information processing systems · Cited by 692 · see citing works

EM-DD: An Improved Multiple-Instance Learning Technique

Zhang, Q., & Goldman, S. A. (2001) · Cited by 626 · see citing works

A Survey on Ensemble Learning for Data Stream Classification

Gomes, H. M., Barddal, J. P., Enembreck, F., & Bifet, A. (2018) · ACM Computing Surveys · doi:10.1145/3054925 · Cited by 579 · see citing works

Solving Multiple-Instance Problem: A Lazy Learning Approach

Wang, J., Zucker, E. J.-D., & Zucker, J. (2000) · CogPrints (University of Southampton) · Cited by 570 · see citing works

Multiple instance classification: Review, taxonomy and comparative study

Amores, J. (2013) · Artificial Intelligence · doi:10.1016/j.artint.2013.06.003 · Cited by 562 · see citing works

Adaptive Learning from Evolving Data Streams

Bifet, A., & Gavaldà, R. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03915-7_22 · Cited by 497 · see citing works

Defect detection in textured materials using Gabor filters

Kumar, A., & Pang, G. K. H. (2002) · IEEE Transactions on Industry Applications · doi:10.1109/28.993164 · Cited by 475 · see citing works

Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

Tao, X., Zhang, D., Ma, W., Liu, X., & Xu, D. (2018) · Applied Sciences · doi:10.3390/app8091575 · Cited by 471 · see citing works

Multi-Instance Kernels

Gärtner, T., Flach, P. A., Kowalczyk, A., & Smola, A. J. (2002) · Cited by 460 · see citing works

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining

(2001) · doi:10.1145/502512 · Cited by 433 · see citing works

Dynamic weighted majority: a new ensemble method for tracking concept drift

Kolter, J. Z., & Maloof, M. A. (2004) · Proceedings · doi:10.1109/icdm.2003.1250911 · Cited by 420 · see citing works

Incremental Learning of Concept Drift from Streaming Imbalanced Data

Ditzler, G., & Robi Polikar, A. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.136 · Cited by 407 · see citing works

A review of multi-instance learning assumptions

Foulds, J. R., & Frank, E. (2010) · The Knowledge Engineering Review · doi:10.1017/s026988890999035x · Cited by 397 · see citing works

Incremental on-line learning: A review and comparison of state of the art algorithms

Losing, V., Hammer, B., & Wersing, H. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.06.084 · Cited by 382 · see citing works

Classifier Ensembles for Changing Environments

Kuncheva, L. I. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-25966-4_1 · Cited by 337 · see citing works

From Machine Learning to Explainable AI

Holzinger, A. (2018) · 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) · doi:10.1109/disa.2018.8490530 · Cited by 306 · see citing works

Learning from streaming data with concept drift and imbalance: an overview

Hoens, T. R., Robi Polikar, A., & Chawla, N. V. (2012) · Progress in Artificial Intelligence · doi:10.1007/s13748-011-0008-0 · Cited by 301 · see citing works

Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning

Ferguson, M., Ak, R., Lee, Y.-T. T., & Law, K. H. (2018) · Smart and Sustainable Manufacturing Systems · doi:10.1520/ssms20180033 · Cited by 280 · see citing works

Proceedings of the Seventeenth International Conference on Machine Learning (ICML-2000)

Pat Langley, B., Francisco, S., Ub, C., & -Hannover, T. S. (2000) · Cited by 248 · see citing works

MILIS: Multiple Instance Learning with Instance Selection

Fu, Z., Robles‐Kelly, A., & Zhou, J. W. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.155 · Cited by 182 · see citing works

Selecting Examples for Partial Memory Learning

Maloof, M. A., & Michalski, R. S. (2000) · Machine Learning · doi:10.1023/a:1007661119649 · Cited by 142 · see citing works

Multiple instance learning with bag dissimilarities

Cheplygina, V., Tax, D. M. J., & Loog, M. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.07.022 · Cited by 139 · see citing works

Negative correlation learning for classification ensembles

Wang, S., Chen, H., & Yao, X. (2010) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596702 · Cited by 107 · see citing works

Online multiple instance boosting for object detection

Qi, Z., Xu, Y., Wang, L.-S., & Song, Y. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.02.011 · Cited by 64 · see citing works

A Comparison of Multi-instance Learning Algorithms

Lin, D. L. (2006) · Research Commons (University of Waikato) · Cited by 48 · see citing works

Learning concept drift in nonstationary environments using an ensemble of classifiers based approach

Karnick, M. T., Ahiskali, M., Muhlbaier, M. D., & Robi Polikar, A. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634290 · Cited by 42 · see citing works

Multiple Instance Learning

Herrera, F., Ventura, S., Bello, R., Chris Cornelis, A., Zafra, A., Sánchez-Tarragó, D., & Vluymans, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-47759-6_2 · Cited by 42 · see citing works

Multiple Instance Learning: Foundations and Algorithms

Herrera, F., Ventura, S., Bello, R., Chris Cornelis, A., Zafra, A., Snchez-Tarrag, D., & Vluymans, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-47759-6 · Cited by 36 · see citing works

Visual tracking with semi-supervised online weighted multiple instance learning

Wang, Z., Yoon, S., Xie, S. J., Lu, Y. F., & Park, D. S. (2015) · The Visual Computer · doi:10.1007/s00371-015-1067-1 · Cited by 25 · see citing works

A Surface Defect Detection Based on Convolutional Neural Network

Wu, X., Cao, K., & Gu, X. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68345-4_17 · Cited by 24 · see citing works

Computer Vision Systems

Vincze, M., Chen, H., & Liu, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68345-4 · Cited by 23 · see citing works

Automated Detection of Welding Discontinuities without Segmentation

Mery, D. (2011) · Americanae (AECID Library) · Cited by 16 · see citing works

Image Analysis and Recognition

Srivastava, A. (2014) · Academic press library in signal processing · doi:10.1016/b978-0-12-396501-1.00009-1 · Cited by 15 · see citing works

Improving Representation of the Positive Class in Imbalanced Multiple-Instance Learning

Mera, C., Orozco‐Alzate, M., & Branch, J. W. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11758-4_29 · Cited by 12 · see citing works

Incremental learning of concept drift from imbalanced data

Ditzler, G. (2011) · Rowan Digitals Works (Rowan University) · Cited by 6 · see citing works

GPU Architecture for Unsupervised Surface Inspection Using Multi-scale Texture Analysis

Weimer, D., Thamer, H., & Thoben, K. (2014) · Procedia Technology · doi:10.1016/j.protcy.2014.09.081 · Cited by 5 · see citing works

Automated Visual Defect Classification for Flat Steel Surface: A Survey

Luo, Q., Fang, X., Su, J., Zhou, J., Zhou, B., Yang, C., Liu, L., Gui, W., & Tian, L. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3030167 · Cited by 148 · see citing works

A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions

Xu, J., Kovatsch, M., Mattern, D., Mazza, F., Harasic, M., Paschke, A., & Lucia, S. (2022) · Applied Sciences · doi:10.3390/app12168239 · Cited by 86 · see citing works

Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection

Ge, C., Wang, J., Wang, J., Qi, Q., Sun, H., & Liao, J. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103232 · Cited by 68 · see citing works

Open set maize seed variety classification using hyperspectral imaging coupled with a dual deep SVDD-based incremental learning framework

Zhang, L., Huang, J., Wei, Y., Liu, J., An, D. U., & Wu, J. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121043 · Cited by 32 · see citing works

A novel FuseDecode Autoencoder for industrial visual inspection: Incremental anomaly detection improvement with gradual transition from unsupervised to mixed-supervision learning with reduced human effort

Kozamernik, N., & Bračun, D. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104198 · Cited by 17 · see citing works

Structured Verification of Machine Learning Models in Industrial Settings

Kaminwar, S. R., Goschenhofer, J., Thomas, J., Thon, I., & Bischl, B. (2022) · Big Data · doi:10.1089/big.2021.0112 · Cited by 15 · see citing works

Adaptive classification using incremental learning for seismic-volcanic signals with concept drift

Castro-Cabrera, P. A., Castellanos-Dominguez, G., Mera, C., Franco, L., & Orozco‐Alzate, M. (2021) · Journal of Volcanology and Geothermal Research · doi:10.1016/j.jvolgeores.2021.107211 · Cited by 12 · see citing works

Embedded intelligence and the data-driven future of application-specific Internet of Things for smart environments

Ang, L.-M., Seng, K. P., & Wachowicz, M. (2022) · International Journal of Distributed Sensor Networks · doi:10.1177/15501329221102371 · Cited by 11 · see citing works

Deep learning object detection applied to defect recognition of memory modules

Huang, J.-T., & Ting, C.-H. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09716-w · Cited by 10 · see citing works

Incremental reinforcement learning for multi-objective analog circuit design acceleration

Abuelnasr, A., Ragab, A., Amer, M., Gosselin, B. J., & Savaria, Y. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107426 · Cited by 8 · see citing works

A group incremental approach for feature selection on hybrid data

Wang, F., Wei, W., & Liang, J. (2022) · Soft Computing · doi:10.1007/s00500-022-06838-x · Cited by 8 · see citing works

Application of concept drift detection and adaptive framework for non linear time series data from cardiac surgery

Ganesan, R., Kaur, T., Mittal, A., Sahi, M., Konar, S., Samra, T., Puri, G. D., Thingnum, S. K. S., & Auluck, N. (2024) · Computational Intelligence · doi:10.1111/coin.12658 · Cited by 2 · see citing works

Distributional Drift Detection in Medical Imaging with Sketching and Fine-Tuned Transformer

Wu, Y., Nguyen, P., Yesha, R., & Yesha, Y. (2025) · IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh67620.2025.00013 · Cited by 1 · see citing works

Machine Learning in Smart Manufacturing: Challenges and Solutions

Adalat, O., Polovina, N., & Konur, S. (2026) · Applied Sciences · doi:10.3390/app16178599 · Cited by 1 · see citing works

Automatic Analysis of People in Thermal Imagery

Liu, J. (2022) · doi:10.54337/aau499830013

Multi-core Parallelization of Point Set Dissimilarities for Accelerating the Comparison of Bags with Many Instances

Villegas-Jaramillo, E. J., Uribe-Hurtado, A.-L., & Orozco‐Alzate, M. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-20859-1_21

Application of concept Drift Detection and Adaptive Framework for Non Linear Time Series Data from Cardiac SurgeryPreprint

Auluck, N., Ganesan, R., Kaur, T., Mittal, A., Sahi, M., Konar, S., Samra, T., Puri, G. D., & Thingnum, S. K. S. (2023) · doi:10.22541/au.168446904.43049204/v1

Neighborhood Component Feature Selection for Multiple Instance Learning Paradigm

Turri, G., & Romeo, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70341-6_14

Automatic visual inspection: An approach with multi-instance learning

Mera, C., Orozco‐Alzate, M., Branch, J. W., & Mery, D. (2016) · Computers in Industry · doi:10.1016/j.compind.2016.09.002 · Cited by 33 · see citing works

Incremental learning in non-stationary environments with concept drift using a multiple classifier based approach

Karnick, M. T., Muhlbaier, M. D., & Robi Polikar, A. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761062 · Cited by 28 · see citing works

A drift detection method for industrial images based on a defect segmentation model

Li, W., Li, B., Wang, Z., Qiu, C., Niu, S., Tan, X., & Niu, T. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112320 · Cited by 10 · see citing works

R.: Incremental learning of concept drift in nonstationary environments

Elwell, R., Robi Polikar, A., & Member, S. (2011)

Incremental learning and concept drift: Editor's introduction

Kubát, M., da Gama, J. M. P., & Utgoff, P. E. (2004) · Intelligent Data Analysis · doi:10.3233/ida-2004-8301 · Cited by 6 · see citing works