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Academic literature on the topic 'Instance weighting'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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Journal articles on the topic "Instance weighting"

1

Lienen, Julian, and Eyke Hüllermeier. "Instance weighting through data imprecisiation." International Journal of Approximate Reasoning 134 (July 2021): 1–14. http://dx.doi.org/10.1016/j.ijar.2021.04.002.

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Derrac, J., I. Triguero, S. Garcia, and F. Herrera. "Integrating Instance Selection, Instance Weighting, and Feature Weighting for Nearest Neighbor Classifiers by Coevolutionary Algorithms." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42, no. 5 (2012): 1383–97. http://dx.doi.org/10.1109/tsmcb.2012.2191953.

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Sundararajan, Karpagam, and Kathiravan Srinivasan. "A Synergistic Optimization Algorithm with Attribute and Instance Weighting Approach for Effective Drought Prediction in Tamil Nadu." Sustainability 16, no. 7 (2024): 2936. http://dx.doi.org/10.3390/su16072936.

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The creation of frameworks for lowering natural hazards is a sustainable development goal specified by the United Nations. This study aims to predict drought occurrence in Tamil Nadu, India, using 26 years of data, with only 3 drought years. Since the drought-occurrence years are minimal, it is an imbalanced dataset, which gives a suboptimal classification performance. The accuracy metric has a tendency to produce misleadingly high results by focusing on the accuracy of forecasting the majority class while ignoring the minority class; hence, this work considers the metrics’ precision and recall. A novel strategy uses attribute (or instance) weighting, which allots weights to attributes (or instances) based on their importance, to improve precision and recall. These weights are found using a bio-inspired optimization algorithm, by designing its fitness function to improve precision and recall of the minority (drought) class. Since increasing precision and recall is a tug-of-war, multi-objective optimization helps to identify optimal attribute (or instance) weight balancing precision and recall while maximizing both. The newly introduced Synergistic Optimization Algorithm (SOA) is utilized for multi-objective optimization in order to ascertain weights for attributes (or instances). In SOA, to solve multi-objective optimization, each objective’s population was generated using three distinct algorithms, namely, the Genetic, Firefly, and Particle Swarm Optimization (PSO) algorithms. The experimental results demonstrated that the prediction performance for the minority drought class was superior when utilizing instance (or attribute) weighting compared to the approach not employing attribute/instance weighting. The Gradient Boosting classifier with an attribute-weighted dataset achieved precision and recall values of 0.92 and 0.79, whereas, with instance weighting, the values were 0.9 and 0.76 for the drought class. The attribute weighting shows that in addition to the default drought indices SPI and SPEI, pollution factors and mean sea level rise are valuable indicators in drought prediction. From instance weighting, it is inferred that the instances of the months of March, April, July, and August contribute most to drought prediction.

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Petrović, Andrija, Mladen Nikolić, Sandro Radovanović, Boris Delibašić, and Miloš Jovanović. "FAIR: Fair adversarial instance re-weighting." Neurocomputing 476 (March 2022): 14–37. http://dx.doi.org/10.1016/j.neucom.2021.12.082.

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Chai, Jing, Hongtao Chen, Lixia Huang, and Fanhua Shang. "Maximum margin multiple-instance feature weighting." Pattern Recognition 47, no. 6 (2014): 2091–103. http://dx.doi.org/10.1016/j.patcog.2013.12.009.

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Lee, Woojin, Jaewook Lee, and Saerom Park. "Instance Weighting Domain Adaptation Using Distance Kernel." Industrial Engineering & Management Systems 17, no. 2 (2018): 334–40. http://dx.doi.org/10.7232/iems.2018.17.2.334.

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Thanathamathee, Putthiporn, Siriporn Sawangarreerak, Siripinyo Chantamunee, and Dinna Nina Mohd Nizam. "SHAP-Instance Weighted and Anchor Explainable AI: Enhancing XGBoost for Financial Fraud Detection." Emerging Science Journal 8, no. 6 (2024): 2404–30. https://doi.org/10.28991/esj-2024-08-06-016.

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This research aims to enhance financial fraud detection by integrating SHAP-Instance Weighting and Anchor Explainable AI with XGBoost, addressing challenges of class imbalance and model interpretability. The study extends SHAP values beyond feature importance to instance weighting, assigning higher weights to more influential instances. This focuses model learning on critical samples. It combines this with Anchor Explainable AI to generate interpretable if-then rules explaining model decisions. The approach is applied to a dataset of financial statements from the listed companies on the Stock Exchange of Thailand. The method significantly improves fraud detection performance, achieving perfect recall for fraudulent instances and substantial gains in accuracy while maintaining high precision. It effectively differentiates between non-fraudulent, fraudulent, and grey area cases. The generated rules provide transparent insights into model decisions, offering nuanced guidance for risk management and compliance. This research introduces instance weighting based on SHAP values as a novel concept in financial fraud detection. By simultaneously addressing class imbalance and interpretability, the integrated approach outperforms traditional methods and sets a new standard in the field. It provides a robust, explainable solution that reduces false positives and increases trust in fraud detection models. Doi: 10.28991/ESJ-2024-08-06-016 Full Text: PDF

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Gondra, Iker, and Tao Xu. "Image region re-weighting via multiple instance learning." Signal, Image and Video Processing 4, no. 4 (2009): 409–17. http://dx.doi.org/10.1007/s11760-009-0128-1.

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Li, Yongming, Yuanlin Zheng, Pin Wang, Xinzheng Zhang, Xiaoping Zeng, and Xinke Li. "Improved Age Estimation Mechanism from Medical Data Based on Deep Instance Weighting Fusion." Journal of Medical Imaging and Health Informatics 10, no. 5 (2020): 984–93. http://dx.doi.org/10.1166/jmihi.2020.3033.

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Age estimation is very useful in the fields of pattern recognition and data mining, especially for medical problems. The current methods of age estimation do not consider the relationships among instances, especially the internal hierarchical structure, which limits the potential improvement of the age estimation error. A deep age estimation mechanism based on deep instance weighting fusion is proposed to solve this problem. First, an iterative means clustering (IMC) algorithm is designed to construct the hierarchical instance space (multiplelayer instance space) and obtain multiple trained regression models. Second, a deep instance weighting fusion (DIWF) mechanism is designed to fuse the results from the trained regression models to produce the final results. The experimental results show that the mean absolute error (MAE) of the estimated ages can be decreased significantly on two publicly available data sets, with relative gains of 4.97% and 0.8% on the Heart Disease Data Set and Diabetes Mellitus Data Set, respectively. Additionally, some factors that may influence the performance of the proposed mechanism are studied. In general, the proposed age estimation mechanism is effective. In addition, the mechanism is not a concrete algorithm but framework algorithm (or mechanism), and can be used to generate various concrete age estimation algorithms, so the mechanism is helpful for related studies.

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Wang, Zhi, Wei Bi, Yan Wang, and Xiaojiang Liu. "Better Fine-Tuning via Instance Weighting for Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7241–48. http://dx.doi.org/10.1609/aaai.v33i01.33017241.

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Transfer learning for deep neural networks has achieved great success in many text classification applications. A simple yet effective transfer learning method is to fine-tune the pretrained model parameters. Previous fine-tuning works mainly focus on the pre-training stage and investigate how to pretrain a set of parameters that can help the target task most. In this paper, we propose an Instance Weighting based Finetuning (IW-Fit) method, which revises the fine-tuning stage to improve the final performance on the target domain. IW-Fit adjusts instance weights at each fine-tuning epoch dynamically to accomplish two goals: 1) identify and learn the specific knowledge of the target domain effectively; 2) well preserve the shared knowledge between the source and the target domains. The designed instance weighting metrics used in IW-Fit are model-agnostic, which are easy to implement for general DNN-based classifiers. Experimental results show that IW-Fit can consistently improve the classification accuracy on the target domain.

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Books on the topic "Instance weighting"

1

Gadsden, Richard J. Measurement of caseload weightings associated with the Children Act. Home Office, 1994.

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Stegenga, Jacob. Assessing Medical Evidence. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198747048.003.0007.

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Medical scientists employ ‘quality assessment tools’ to assess evidence from medical research, especially from randomized trials. These tools are designed to take into account methodological details of studies, including randomization, subject allocation concealment, and other features of studies deemed relevant to minimizing bias. There are dozens of such tools available. They differ widely from each other, and empirical studies show that they have low inter-rater reliability and low inter-tool reliability. This is an instance of a more general problem called here the underdetermination of evidential significance. Disagreements about the quality of evidence can be due to different—but in principle equally good—weightings of the methodological features that constitute quality assessment tools. Thus, the malleability of empirical research in medicine is deep: in addition to the malleability of first-order empirical methods, such as randomized trials, there is malleability in the tools used to evaluate first-order methods.

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Book chapters on the topic "Instance weighting"

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Wang, Jindong, and Yiqiang Chen. "Instance Weighting Methods." In Introduction to Transfer Learning. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-7584-4_4.

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Ji, Qiang, Liangxiao Jiang, and Wenjun Zhang. "Instance Weighting-Based Noise Correction for Crowdsourcing." In Lecture Notes in Computer Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4752-2_24.

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Ting, Kai Ming. "Inducing cost-sensitive trees via instance weighting." In Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0094814.

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Blachnik, Marcin, and Włodzisław Duch. "Improving Accuracy of LVQ Algorithm by Instance Weighting." In Artificial Neural Networks – ICANN 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15825-4_31.

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Kwek, Stephen, and Chau Nguyen. "iBoost: Boosting Using an instance-Based Exponential Weighting Scheme." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36755-1_21.

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Shrewsbury, Daniel, Suneung Kim, Young-Eun Kim, Heejo Kong, and Seong-Whan Lee. "Instance-Ambiguity Weighting for Multi-label Recognition with Limited Annotations." In Advances in Knowledge Discovery and Data Mining. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2242-6_13.

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Prat, Gabriel, and Lluís A. Belanche. "Improved Stability of Feature Selection by Combining Instance and Feature Weighting." In Research and Development in Intelligent Systems XXXI. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12069-0_3.

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Qaiser, Talha, Stefan Winzeck, Theodore Barfoot, et al. "Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87234-2_74.

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Derrac, Joaquín, Isaac Triguero, Salvador García, and Francisco Herrera. "A Co-evolutionary Framework for Nearest Neighbor Enhancement: Combining Instance and Feature Weighting with Instance Selection." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28931-6_17.

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Son, Jeong-Woo, Hyun-Je Song, Seong-Bae Park, and Se-Young Park. "Coping with Distribution Change in the Same Domain Using Similarity-Based Instance Weighting." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05224-8_27.

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Conference papers on the topic "Instance weighting"

1

Li, Fei, and Rujie Liu. "Graph-based multiple-instance learning with instance weighting for image retrieval." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6116156.

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Wu, Yongcheng. "A New Instance-weighting Naive Bayes Text Classifiers." In 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE). IEEE, 2018. http://dx.doi.org/10.1109/irce.2018.8492960.

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Gong, Jen J., Thoralf M. Sundt, James D. Rawn, and John V. Guttag. "Instance Weighting for Patient-Specific Risk Stratification Models." In KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015. http://dx.doi.org/10.1145/2783258.2783397.

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Ramirez-Cruz, Jose-federico, Olac Fuentes, Vicente Alarcon-Aquino, and Luciano Garcia-Banuelos. "Instance Selection and Feature Weighting Using Evolutionary Algorithms." In 2006 15th International Conference on Computing. IEEE, 2006. http://dx.doi.org/10.1109/cic.2006.42.

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Wang, Rui, Masao Utiyama, Lemao Liu, Kehai Chen, and Eiichiro Sumita. "Instance Weighting for Neural Machine Translation Domain Adaptation." In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/d17-1155.

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Xia, Rui, Zhenchun Pan, and Feng Xu. "Instance Weighting with Applications to Cross-domain Text Classification via Trading off Sample Selection Bias and Variance." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/624.

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Domain adaptation is an important problem in natural language processing (NLP) due to the distributional difference between the labeled source domain and the target domain. In this paper, we study the domain adaptation problem from the instance weighting perspective. By using density ratio as the instance weight, the traditional instance weighting approaches can potentially correct the sample selection bias in domain adaptation. However, researchers often failed to achieve good performance when applying instance weighting to domain adaptation in NLP and many negative results were reported in the literature. In this work, we conduct an in-depth study on the causes of the failure, and find that previous work only focused on reducing the sample selection bias, but ignored another important factor, sample selection variance, in domain adaptation. On this basis, we propose a new instance weighting framework by trading off two factors in instance weight learning. We evaluate our approach on two cross-domain text classification tasks and compare it with eight instance weighting methods. The results prove our approach's advantages in domain adaptation performance, optimization efficiency and parameter stability.

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Wu, Jia, Shirui Pan, Zhihua Cai, Xingquan Zhu, and Chengqi Zhang. "Dual instance and attribute weighting for Naive Bayes classification." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889572.

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Zhou, Qin, Shibao Zheng, Hua Yang, Yu Wang, and Hang Su. "Joint instance and feature importance re-weighting for person reidentification." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7471936.

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Li, Irene, Prithviraj Sen, Huaiyu Zhu, Yunyao Li, and Dragomir Radev. "Improving Cross-lingual Text Classification with Zero-shot Instance-Weighting." In Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021). Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.repl4nlp-1.1.

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Biçici, Ergun. "Instance Weighting in Neural Networks for Click-Through Rate Prediction." In 2023 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 2023. http://dx.doi.org/10.1109/asyu58738.2023.10296657.

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