Log in

Relevant bibliographies by topics / Instance weighting / Journal articles

To see the other types of publications on this topic, follow the link: Instance weighting.

Journal articles on the topic 'Instance weighting'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Instance weighting.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

11

Karmaker, Amitava, Kihoon Yoon, Chau Nguyen, and Stephen Kwek. "iBoost: Boosting using an instance-based exponential weighting scheme." International Journal of Hybrid Intelligent Systems 4, no. 4 (2007): 243–54. http://dx.doi.org/10.3233/his-2007-4404.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Kai Ming Ting. "An instance-weighting method to induce cost-sensitive trees." IEEE Transactions on Knowledge and Data Engineering 14, no. 3 (2002): 659–65. http://dx.doi.org/10.1109/tkde.2002.1000348.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Duan, Zhiyi, Limin Wang, Shenglei Chen, and Minghui Sun. "Instance-based weighting filter for superparent one-dependence estimators." Knowledge-Based Systems 203 (September 2020): 106085. http://dx.doi.org/10.1016/j.knosys.2020.106085.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Zhao, Huimin. "Instance weighting versus threshold adjusting for cost-sensitive classification." Knowledge and Information Systems 15, no. 3 (2007): 321–34. http://dx.doi.org/10.1007/s10115-007-0079-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Bouguelia, Mohamed-Rafik, Yolande Belaïd, and Abdel Belaïd. "An adaptive streaming active learning strategy based on instance weighting." Pattern Recognition Letters 70 (January 2016): 38–44. http://dx.doi.org/10.1016/j.patrec.2015.11.010.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Yatsko, Andrew. "Weighting features by the value displacement rebound." Artificial Intelligence Research 9, no. 1 (2020): 27. http://dx.doi.org/10.5430/air.v9n1p27.

Full text

Abstract:

Learning from examples draws on similarity, a concept which formalisation leads to the notion of instance space. Continuous spaces are easier to embrace since, unlike discrete, they often can be seen as hyper-constructs of 3D. Unsurprisingly, the instance-based learning methods are more developed for continuous domains than for discrete ones. The value difference metric (VDM) is one of the few examples of metrics for discrete spaces. Mixed reports about utility of VDM exist. In this paper VDM is compared with another approach where data features are weighted by the Information Gain. Some vulnerabilities of VDM are identified. A weighting method, nothing like VDM, although inspired by the former, is proposed. The results are in favour of the new weighting scheme with illustration of utility for health diagnostics.

APA, Harvard, Vancouver, ISO, and other styles

17

Tarkhan, Aliasghar, Trung Kien Nguyen, Noah Simon, and Jian Dai. "Survival Prediction via Deep Attention-Based Multiple-Instance Learning Networks with Instance Sampling." Proceedings of the AAAI Symposium Series 2, no. 1 (2024): 482–89. http://dx.doi.org/10.1609/aaaiss.v2i1.27717.

Full text

Abstract:

Survival prediction via training deep neural networks with giga-pixel whole-slide images (WSIs) is challenging due to the lack of time annotation at the pixel level or patch (instance). Multiple instance learning (MIL), as a typical weakly supervised learning method, aims to resolve this challenge by using only the slide-level time. The attention-based MIL method leverages and enhances performance by weighting the instances based on their contribution to predicting the outcome. A WSI typically contains hundreds of thousands of image patches. Training a deep neural network with thousands of image patches per slide is computationally expensive and time-consuming. To tackle this issue, we propose an adaptive-learning strategy where we sample a subset of informative instances/patches more often to train the deep survival neural networks. We also present other sampling strategies and compare them with our proposed sampling strategy. Using both real-world and synthesized WSIs for survival, we show that sampling strategies significantly can significantly reduce computing time while result in no or negligible performance loss. We also discuss the benefits of each instance sampling strategy in different scenarios.

APA, Harvard, Vancouver, ISO, and other styles

18

Chapman-Rounds, Matt, Umang Bhatt, Erik Pazos, Marc-Andre Schulz, and Konstantinos Georgatzis. "FIMAP: Feature Importance by Minimal Adversarial Perturbation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 11433–41. http://dx.doi.org/10.1609/aaai.v35i13.17362.

Full text

Abstract:

Instance-based model-agnostic feature importance explanations (LIME, SHAP, L2X) are a popular form of algorithmic transparency. These methods generally return either a weighting or subset of input features as an explanation for the classification of an instance. An alternative literature argues instead that counterfactual instances, which alter the black-box model's classification, provide a more actionable form of explanation. We present Feature Importance by Minimal Adversarial Perturbation (FIMAP), a neural network based approach that unifies feature importance and counterfactual explanations. We show that this approach combines the two paradigms, recovering the output of feature-weighting methods in continuous feature spaces, whilst indicating the direction in which the nearest counterfactuals can be found. Our method also provides an implicit confidence estimate in its own explanations, something existing methods lack. Additionally, FIMAP improves upon the speed of sampling-based methods, such as LIME, by an order of magnitude, allowing for explanation deployment in time-critical applications. We extend our approach to categorical features using a partitioned Gumbel layer and demonstrate its efficacy on standard datasets.

APA, Harvard, Vancouver, ISO, and other styles

19

JIANG, LIANGXIAO, DIANHONG WANG, and ZHIHUA CAI. "DISCRIMINATIVELY WEIGHTED NAIVE BAYES AND ITS APPLICATION IN TEXT CLASSIFICATION." International Journal on Artificial Intelligence Tools 21, no. 01 (2012): 1250007. http://dx.doi.org/10.1142/s0218213011004770.

Full text

Abstract:

Many approaches are proposed to improve naive Bayes by weakening its conditional independence assumption. In this paper, we work on the approach of instance weighting and propose an improved naive Bayes algorithm by discriminative instance weighting. We called it Discriminatively Weighted Naive Bayes. In each iteration of it, different training instances are discriminatively assigned different weights according to the estimated conditional probability loss. The experimental results based on a large number of UCI data sets validate its effectiveness in terms of the classification accuracy and AUC. Besides, the experimental results on the running time show that our Discriminatively Weighted Naive Bayes performs almost as efficiently as the state-of-the-art Discriminative Frequency Estimate learning method, and significantly more efficient than Boosted Naive Bayes. At last, we apply the idea of discriminatively weighted learning in our algorithm to some state-of-the-art naive Bayes text classifiers, such as multinomial naive Bayes, complement naive Bayes and the one-versus-all-but-one model, and have achieved remarkable improvements.

APA, Harvard, Vancouver, ISO, and other styles

20

Chen, Weihao, Lumei Su, Zhiwei Lin, Xinqiang Chen, and Tianyou Li. "Instance Segmentation of Irregular Deformable Objects for Power Operation Monitoring Based on Multi-Instance Relation Weighting Module." Electronics 12, no. 9 (2023): 2126. http://dx.doi.org/10.3390/electronics12092126.

Full text

Abstract:

Electric power operation is necessary for the development of power grid companies, where the safety monitoring of electric power operation is difficult. Irregular deformable objects commonly used in electrical construction, such as safety belts and seines, have a dynamic geometric appearance which leads to the poor performance of traditional detection methods. This paper proposes an end-to-end instance segmentation method using the multi-instance relation weighting module for irregular deformable objects. To solve the problem of introducing redundant background information when using the horizontal rectangular box detector, the Mask Scoring R-CNN is used to perform pixel-level instance segmentation so that the bounding box can accurately surround the irregular objects. Considering that deformable objects in power operation workplaces often appear with construction personnel and the objects have an apparent correlation, a multi-instance relation weighting module is proposed to fuse the appearance features and geometric features of objects so that the relation features between objects are learned end-to-end to improve the segmentation effect of irregular objects. The segmentation mAP on the self-built dataset of irregular deformable objects for electric power operation workplaces reached up to 44.8%. With the same 100,000 training rounds, the bounding box mAP and segmentation mAP improved by 1.2% and 0.2%, respectively, compared with the MS R-CNN. Finally, in order to further verify the generalization performance and practicability of the proposed method, an intelligent monitoring system for the power operation scenes is designed to realize the actual deployment and application of the proposed method. Various tests show that the proposed method can segment irregular deformable objects well.

APA, Harvard, Vancouver, ISO, and other styles

21

Ma, Zong-fang, Zhe Liu, Chan Luo, and Lin Song. "Evidential classification of incomplete instance based on K-nearest centroid neighbor." Journal of Intelligent & Fuzzy Systems 41, no. 6 (2021): 7101–15. http://dx.doi.org/10.3233/jifs-210991.

Full text

Abstract:

Classification of incomplete instance is a challenging problem due to the missing features generally cause uncertainty in the classification result. A new evidential classification method of incomplete instance based on adaptive imputation thanks to the framework of evidence theory. Specifically, the missing values of different incomplete instances in test set are adaptively estimated based on Shannon entropy and K-nearest centroid neighbors (KNCNs) technology. The single or multiple edited instances (with estimations) then are classified by the chosen classifier to get single or multiple classification results for the instances with different discounting (weighting) factors, and a new adaptive global fusion method finally is proposed to unify the different discounted results. The proposed method can well capture the imprecision degree of classification by submitting the instances that are difficult to be classified into a specific class to associate the meta-class and effectively reduce the classification error rates. The effectiveness and robustness of the proposed method has been tested through four experiments with artificial and real datasets.

APA, Harvard, Vancouver, ISO, and other styles

22

Zhu, Zonghai, Zhe Wang, Dongdong Li, Wenli Du, and Yangming Zhou. "Multiple Partial Empirical Kernel Learning with Instance Weighting and Boundary Fitting." Neural Networks 123 (March 2020): 26–37. http://dx.doi.org/10.1016/j.neunet.2019.11.019.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Blachnik, Marcin, and Włodzisław Duch. "LVQ algorithm with instance weighting for generation of prototype-based rules." Neural Networks 24, no. 8 (2011): 824–30. http://dx.doi.org/10.1016/j.neunet.2011.05.013.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Xu, Wenqiang, Liangxiao Jiang, and Liangjun Yu. "An attribute value frequency-based instance weighting filter for naive Bayes." Journal of Experimental & Theoretical Artificial Intelligence 31, no. 2 (2018): 225–36. http://dx.doi.org/10.1080/0952813x.2018.1544284.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Parvinnia, E., M. R. Moosavi, M. Zolghadri Jahromi, and M. H. Sadreddini. "A Robust Instance Weighting Technique for Nearest Neighbor Classification in Noisy Environments." Indian Journal of Science and Technology 8, no. 1 (2015): 70. http://dx.doi.org/10.17485/ijst/2015/v8i1/56659.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Yu, Kai, Xiaowei Xu, Martin Ester, and Hans-Peter Kriegel. "Feature Weighting and Instance Selection for Collaborative Filtering: An Information-Theoretic Approach*." Knowledge and Information Systems 5, no. 2 (2003): 201–24. http://dx.doi.org/10.1007/s10115-003-0089-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Kang, Seokho. "k-Nearest Neighbor Learning with Graph Neural Networks." Mathematics 9, no. 8 (2021): 830. http://dx.doi.org/10.3390/math9080830.

Full text

Abstract:

k-nearest neighbor (kNN) is a widely used learning algorithm for supervised learning tasks. In practice, the main challenge when using kNN is its high sensitivity to its hyperparameter setting, including the number of nearest neighbors k, the distance function, and the weighting function. To improve the robustness to hyperparameters, this study presents a novel kNN learning method based on a graph neural network, named kNNGNN. Given training data, the method learns a task-specific kNN rule in an end-to-end fashion by means of a graph neural network that takes the kNN graph of an instance to predict the label of the instance. The distance and weighting functions are implicitly embedded within the graph neural network. For a query instance, the prediction is obtained by performing a kNN search from the training data to create a kNN graph and passing it through the graph neural network. The effectiveness of the proposed method is demonstrated using various benchmark datasets for classification and regression tasks.

APA, Harvard, Vancouver, ISO, and other styles

28

Yu, Liangjun, Shengfeng Gan, Yu Chen, and Dechun Luo. "A Novel Hybrid Approach: Instance Weighted Hidden Naive Bayes." Mathematics 9, no. 22 (2021): 2982. http://dx.doi.org/10.3390/math9222982.

Full text

Abstract:

Naive Bayes (NB) is easy to construct but surprisingly effective, and it is one of the top ten classification algorithms in data mining. The conditional independence assumption of NB ignores the dependency between attributes, so its probability estimates are often suboptimal. Hidden naive Bayes (HNB) adds a hidden parent to each attribute, which can reflect dependencies from all the other attributes. Compared with other Bayesian network algorithms, it offers significant improvements in classification performance and avoids structure learning. However, the assumption that HNB regards each instance equivalent in terms of probability estimation is not always true in real-world applications. In order to reflect different influences of different instances in HNB, the HNB model is modified into the improved HNB model. The novel hybrid approach called instance weighted hidden naive Bayes (IWHNB) is proposed in this paper. IWHNB combines instance weighting with the improved HNB model into one uniform framework. Instance weights are incorporated into the improved HNB model to calculate probability estimates in IWHNB. Extensive experimental results show that IWHNB obtains significant improvements in classification performance compared with NB, HNB and other state-of-the-art competitors. Meanwhile, IWHNB maintains the low time complexity that characterizes HNB.

APA, Harvard, Vancouver, ISO, and other styles

29

Lee, Kihoon, Soonyoung Han, Van Huan Pham, et al. "Multi-Objective Instance Weighting-Based Deep Transfer Learning Network for Intelligent Fault Diagnosis." Applied Sciences 11, no. 5 (2021): 2370. http://dx.doi.org/10.3390/app11052370.

Full text

Abstract:

Fault diagnosis is a top-priority task for the health management of manufacturing processes. Deep learning-based methods are widely used to secure high fault diagnosis accuracy. Actually, it is difficult and expensive to collect large-scale data in industrial fields. Several prerequisite problems can be solved using transfer learning for fault diagnosis. Data from the source domain that are different but related to the target domain are used to increase the diagnosis performance of the target domain. However, a negative transfer occurs that degrades diagnosis performance due to the transfer when the discrepancy between and within domains is large. A multi-objective instance weighting-based transfer learning network is proposed to solve this problem and successfully applied to fault diagnosis. The proposed method uses a newly devised multi-objective instance weight to deal with practical situations where domain discrepancy is large. It adjusts the influence of the domain data on model training through two theoretically different indicators. Knowledge transfer is performed differentially by sorting instances similar to the target domain in terms of distribution with useful information for the target task. This domain optimization process maximizes the performance of transfer learning. A case study using an industrial robot and spot-welding testbed is conducted to verify the effectiveness of the proposed technique. The performance and applicability of transfer learning in the proposed method are observed in detail through the same case study as the actual industrial field for comparison. The diagnostic accuracy and robustness are high, even when few data are used. Thus, the proposed technique is a promising tool that can be used for successful fault diagnosis.

APA, Harvard, Vancouver, ISO, and other styles

30

Cai, Shaowei, Kaile Su, and Qingliang Chen. "EWLS: A New Local Search for Minimum Vertex Cover." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 45–50. http://dx.doi.org/10.1609/aaai.v24i1.7539.

Full text

Abstract:

A number of algorithms have been proposed for the Minimum Vertex Cover problem. However, they are far from satisfactory, especially on hard instances. In this paper, we introduce Edge Weighting Local Search (EWLS), a new local search algorithm for the Minimum Vertex Cover problem. EWLS is based on the idea of extending a partial vertex cover into a vertex cover. A key point of EWLS is to find a vertex set that provides a tight upper bound on the size of the minimum vertex cover. To this purpose, EWLS employs an iterated local search procedure, using an edge weighting scheme which updates edge weights when stuck in local optima. Moreover, some sophisticated search strategies have been taken to improve the quality of local optima. Experimental results on the broadly used DIMACS benchmark show that EWLS is competitive with the current best heuristic algorithms, and outperforms them on hard instances. Furthermore, on a suite of difficult benchmarks, EWLS delivers the best results and sets a new record on the largest instance.

APA, Harvard, Vancouver, ISO, and other styles

31

Tang, Xijia, Chao Xu, Tingjin Luo, and Chenping Hou. "Multi-instance positive and unlabeled learning with bi-level embedding." Intelligent Data Analysis 26, no. 3 (2022): 659–78. http://dx.doi.org/10.3233/ida-215896.

Full text

Abstract:

Multiple Instance Learning (MIL) is a widely studied learning paradigm which arises from real applications. Existing MIL methods have achieved prominent performances under the premise of plenty annotation data. Nevertheless, sufficient labeled data is often unattainable due to the high labeling cost. For example, the task in web image identification is to find similar samples among a large size of unlabeled dataset through a small number of provided target pictures. This leads to a particular scenario of Multiple Instance Learning with insufficient Positive and superabundant Unlabeled data (PU-MIL), which is a hot research topic in MIL recently. In this paper, we propose a novel method called Multiple Instance Learning with Bi-level Embedding (MILBLE) to tackle PU-MIL problem. Unlike other PU-MIL method using only simple single-level mapping, the bi-level embedding strategy are designed to customize specific mapping for positive and unlabeled data. It ensures the characteristics of key instance are not erased. Moreover, the weighting measure adopted in positive data can extracts the uncontaminated information of true positive instances without interference from negative ones. Finally, we minimize the classification error loss of mapped examples based on class-prior probability to train the optimal classifier. Experimental results show that our method has better performance than other state-of-the-art methods.

APA, Harvard, Vancouver, ISO, and other styles

32

TAHMORESNEZHAD, Jafar, and Sattar HASHEMI. "Exploiting kernel-based feature weighting and instance clustering to transfer knowledge across domains." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 25 (2017): 292–307. http://dx.doi.org/10.3906/elk-1503-245.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Varpa, Kirsi, Kati Iltanen, Markku Siermala, and Martti Juhola. "Attribute weighting with Scatter and instance-based learning methods evaluated with otoneurological data." International Journal of Data Science 2, no. 3 (2017): 173. http://dx.doi.org/10.1504/ijds.2017.086257.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Iltanen, Kati, Markku Siermala, Martti Juhola, and Kirsi Varpa. "Attribute weighting with Scatter and instance-based learning methods evaluated with otoneurological data." International Journal of Data Science 2, no. 3 (2017): 173. http://dx.doi.org/10.1504/ijds.2017.10007392.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Lay, Nathan, Yohannes Tsehay, Matthew D. Greer, et al. "Detection of prostate cancer in multiparametric MRI using random forest with instance weighting." Journal of Medical Imaging 4, no. 2 (2017): 024506. http://dx.doi.org/10.1117/1.jmi.4.2.024506.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Pérez-Rodríguez, Javier, Alexis Germán Arroyo-Peña, and Nicolás García-Pedrajas. "Simultaneous instance and feature selection and weighting using evolutionary computation: Proposal and study." Applied Soft Computing 37 (December 2015): 416–43. http://dx.doi.org/10.1016/j.asoc.2015.07.046.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Liu, Anjin, Jie Lu, and Guangquan Zhang. "Diverse Instance-Weighting Ensemble Based on Region Drift Disagreement for Concept Drift Adaptation." IEEE Transactions on Neural Networks and Learning Systems 32, no. 1 (2021): 293–307. http://dx.doi.org/10.1109/tnnls.2020.2978523.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Hu, Xiang, and Guo Yong Huang. "The Application of Improved Efficiency Coefficient Method in Debris Flow Warning with Rock Technology in Civil Engineering." Applied Mechanics and Materials 454 (October 2013): 149–52. http://dx.doi.org/10.4028/www.scientific.net/amm.454.149.

Full text

Abstract:

To solve the problem of inaccurate prediction of debris flow in dangerous rock technology, introducing combination weighting method to improve the accuracy. Calculate the weight of each impact factors by using improved AHP (analytical hierarchy process) and entropy method. Then use combination weighting method to put the two sets of weight into a new weight. Finally apply efficacy coefficient method quantitative assessment the risk of the debris flow. The instance results show: using Efficacy Coefficient method that combined the Combination Weighting method can reflect the actual situation of debris flows, and have better effect than using entropy method. And it provides an idea of the method on disaster prevention and mitigation and dangerous rock technology.

APA, Harvard, Vancouver, ISO, and other styles

39

Cichosz, Paweł, Stanisław Kozdrowski, and Sławomir Sujecki. "Learning to Classify DWDM Optical Channels from Tiny and Imbalanced Data." Entropy 23, no. 11 (2021): 1504. http://dx.doi.org/10.3390/e23111504.

Full text

Abstract:

Applying machine learning algorithms for assessing the transmission quality in optical networks is associated with substantial challenges. Datasets that could provide training instances tend to be small and heavily imbalanced. This requires applying imbalanced compensation techniques when using binary classification algorithms, but it also makes one-class classification, learning only from instances of the majority class, a noteworthy alternative. This work examines the utility of both these approaches using a real dataset from a Dense Wavelength Division Multiplexing network operator, gathered through the network control plane. The dataset is indeed of a very small size and contains very few examples of “bad” paths that do not deliver the required level of transmission quality. Two binary classification algorithms, random forest and extreme gradient boosting, are used in combination with two imbalance handling methods, instance weighting and synthetic minority class instance generation. Their predictive performance is compared with that of four one-class classification algorithms: One-class SVM, one-class naive Bayes classifier, isolation forest, and maximum entropy modeling. The one-class approach turns out to be clearly superior, particularly with respect to the level of classification precision, making it possible to obtain more practically useful models.

APA, Harvard, Vancouver, ISO, and other styles

40

骆, 洁琴. "A Locally Instance Weighting Naive Bayes Text Classification Algorithm Based on Distance Correlation Coefficient." Advances in Applied Mathematics 13, no. 06 (2024): 2901–11. http://dx.doi.org/10.12677/aam.2024.136278.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Chai, Jing, Zehua Chen, Hongtao Chen, and Xinghao Ding. "Designing bag-level multiple-instance feature-weighting algorithms based on the large margin principle." Information Sciences 367-368 (November 2016): 783–808. http://dx.doi.org/10.1016/j.ins.2016.07.029.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Hu, Keli, Wei He, Jun Ye, Liping Zhao, Hua Peng, and Jiatian Pi. "Online Visual Tracking of Weighted Multiple Instance Learning via Neutrosophic Similarity-Based Objectness Estimation." Symmetry 11, no. 6 (2019): 832. http://dx.doi.org/10.3390/sym11060832.

Full text

Abstract:

An online neutrosophic similarity-based objectness tracking with a weighted multiple instance learning algorithm (NeutWMIL) is proposed. Each training sample is extracted surrounding the object location, and the distribution of these samples is symmetric. To provide a more robust weight for each sample in the positive bag, the asymmetry of the importance of the samples is considered. The neutrosophic similarity-based objectness estimation with object properties (super straddling) is applied. The neutrosophic theory is a new branch of philosophy for dealing with incomplete, indeterminate, and inconsistent information. By considering the surrounding information of the object, a single valued neutrosophic set (SVNS)-based segmentation parameter selection method is proposed, to produce a well-built set of superpixels which can better explain the object area at each frame. Then, the intersection and shape-distance criteria are proposed for weighting each superpixel in the SVNS domain, mainly via three membership functions, T (truth), I (indeterminacy), and F (falsity), for each criterion. After filtering out the superpixels with low response, the newly defined neutrosophic weights are utilized for weighting each sample. Furthermore, the objectness estimation information is also applied for estimating and alleviating the problem of tracking drift. Experimental results on challenging benchmark video sequences reveal the superior performance of our algorithm when confronting appearance changes and background clutters.

APA, Harvard, Vancouver, ISO, and other styles

43

Hockenberry, Adam J., and Claus O. Wilke. "Phylogenetic Weighting Does Little to Improve the Accuracy of Evolutionary Coupling Analyses." Entropy 21, no. 10 (2019): 1000. http://dx.doi.org/10.3390/e21101000.

Full text

Abstract:

Homologous sequence alignments contain important information about the constraints that shape protein family evolution. Correlated changes between different residues, for instance, can be highly predictive of physical contacts within three-dimensional structures. Detecting such co-evolutionary signals via direct coupling analysis is particularly challenging given the shared phylogenetic history and uneven sampling of different lineages from which protein sequences are derived. Current best practices for mitigating such effects include sequence-identity-based weighting of input sequences and post-hoc re-scaling of evolutionary coupling scores. However, numerous weighting schemes have been previously developed for other applications, and it is unknown whether any of these schemes may better account for phylogenetic artifacts in evolutionary coupling analyses. Here, we show across a dataset of 150 diverse protein families that the current best practices out-perform several alternative sequence- and tree-based weighting methods. Nevertheless, we find that sequence weighting in general provides only a minor benefit relative to post-hoc transformations that re-scale the derived evolutionary couplings. While our findings do not rule out the possibility that an as-yet-untested weighting method may show improved results, the similar predictive accuracies that we observe across conceptually distinct weighting methods suggests that there may be little room for further improvement on top of existing strategies.

APA, Harvard, Vancouver, ISO, and other styles

44

Sanodiya, Rakesh Kumar, and Leehter Yao. "A Subspace Based Transfer Joint Matching with Laplacian Regularization for Visual Domain Adaptation." Sensors 20, no. 16 (2020): 4367. http://dx.doi.org/10.3390/s20164367.

Full text

Abstract:

In a real-world application, the images taken by different cameras with different conditions often incur illumination variation, low-resolution, different poses, blur, etc., which leads to a large distribution difference or gap between training (source) and test (target) images. This distribution gap is challenging for many primitive machine learning classification and clustering algorithms such as k-Nearest Neighbor (k-NN) and k-means. In order to minimize this distribution gap, we propose a novel Subspace based Transfer Joint Matching with Laplacian Regularization (STJML) method for visual domain adaptation by jointly matching the features and re-weighting the instances across different domains. Specifically, the proposed STJML-based method includes four key components: (1) considering subspaces of both domains; (2) instance re-weighting; (3) it simultaneously reduces the domain shift in both marginal distribution and conditional distribution between the source domain and the target domain; (4) preserving the original similarity of data points by using Laplacian regularization. Experiments on three popular real-world domain adaptation problem datasets demonstrate a significant performance improvement of our proposed method over published state-of-the-art primitive and domain adaptation methods.

APA, Harvard, Vancouver, ISO, and other styles

45

Zhao, Qing Liang, Hua Qing Wang, and Jin Ji Gao. "Study on Dynamic Balance Weighting for Single-Disk Rotor System Based on Phase Difference Mapping." Advanced Materials Research 430-432 (January 2012): 1437–41. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.1437.

Full text

Abstract:

The rotor mass imbalance is main reason of rotating mechanical vibration. A new dynamic balance weighting method for single-disk rotor system based on phase difference mapping is presented. Firstly, the influence coefficient method and its characteristics are analyzed in detail. Secondly, the equivalent phase difference mapping relationship between incentive and vibration response for single-disk rotor system is proved by differential equations and Laplace transform theory. Finally, a specific application instance is showed. The new method is simple and easy to peel the phase coupling relationship between incentive and response, which can be used to guide dynamic balance weighting for single-disk rotor system on site.

APA, Harvard, Vancouver, ISO, and other styles

46

Barragán, E., E. Garcia, C. Minchala, and E. Zalamea. "Technical, economic, and environmental analysis to define the conditions for the implementation of charge stations, a case study in the city of Azogues - Ecuador." Renewable Energy and Power Quality Journal 21, no. 1 (2023): 701–6. http://dx.doi.org/10.24084/repqj21.454.

Full text

Abstract:

Reducing environmental pollution caused by traditional mobility solutions, such as combustion vehicles, requires a deep dive into sustainable mobility. Consequently, promoting electric vehicles (EVs) constitutes a long-hanging fruit for establishing a route map for decarbonizing city mobility. Nevertheless, several tasks require urgent attention to allow EV technology to deploy in cities. For instance, optimal locations of charging stations for EVs by considering a multicriteria approach are urgently required. This work studies the most feasible alternatives for locating and implementing EV chargers (EVC) in Azogues, Ecuador. This study applies Promethee multicriteria method, considering three different location alternatives in the city and four criteria that will be divided into ten subcriteria for the analysis. First, the multi-objective function´s weighting factors were established using three weighting methods: equal weighting, point allocation, and critical weighting. Once the matrix of sub-criteria with their weights was established, Visual Promethee, which works with Promethee II, was applied to establish the optimal candidate site. The results of this optimization analysis indicate an optimal location for the EVC.

APA, Harvard, Vancouver, ISO, and other styles

47

Liu, Xinhui, Jifa Chen, and Qiubo Huang. "Instance-Level Weighted Contrast Learning for Text Classification." Applied Sciences 15, no. 8 (2025): 4236. https://doi.org/10.3390/app15084236.

Full text

Abstract:

With the explosion of information, the amount of text data has increased significantly, making text categorization a central area of research in natural language processing (NLP). Traditional machine learning methods are effective, but deep learning models excel in processing semantic information. Models such as CNN, RNN, LSTM, and GRU have emerged as powerful tools for text classification. Pre-trained models such as BERT and GPT have further advanced text categorization techniques. Contrastive learning has become a key research focus aimed at improving classification performance by learning the similarities and differences between samples using models. However, existing contrastive learning methods have notable shortcomings, primarily concerning insufficient data utilization. This study focuses on data enhancement techniques to expand the text data through symbol insertion, affirmative auxiliary verbs, double negation, and punctuation repetition, aiming to improve the generalization and robustness of the pre-trained model. Two data enhancement strategies, affirmative enhancement and negative transformation, are introduced to deepen the data’s meaning and increase the volume of training data. To address the introduction of false data, an instance weighting method is employed to penalize false negative samples, while complementary models generate sample weights to mitigate the impact of sampling bias. Finally, the effectiveness of the proposed method is demonstrated through several experiments.

APA, Harvard, Vancouver, ISO, and other styles

48

Bao, Yue, Yu Ming Li, and Bo Zhao. "The Research about Sorting Methods of Quality Curriculum Assessment Based on Interval Numbers." Advanced Materials Research 926-930 (May 2014): 3633–36. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3633.

Full text

Abstract:

Utilizing interval number theory, this paper explores a sorting method to assess quality curriculum. Based on comprehensive analysis of a variety of factors affecting quality curriculum, using combination weighting methods of objective and subjective, we give the corresponding weight interval values of evaluation indicators. Furthermore, we give the sorting steps of quality curriculum assessment based on interval numbers, and give an application instance.

APA, Harvard, Vancouver, ISO, and other styles

49

Wootten, Adrienne M., Elias C. Massoud, Duane E. Waliser, and Huikyo Lee. "Assessing sensitivities of climate model weighting to multiple methods, variables, and domains in the south-central United States." Earth System Dynamics 14, no. 1 (2023): 121–45. http://dx.doi.org/10.5194/esd-14-121-2023.

Full text

Abstract:

Abstract. Given the increasing use of climate projections and multi-model ensemble weighting for a diverse array of applications, this project assesses the sensitivities of climate model weighting strategies and their resulting ensemble means to multiple components, such as the weighting schemes, climate variables, or spatial domains of interest. The purpose of this study is to assess the sensitivities associated with multi-model weighting strategies. The analysis makes use of global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their statistically downscaled counterparts created with the localized constructed analogs (LOCA) method. This work focuses on historical and projected future mean precipitation and daily high temperatures of the south-central United States. Results suggest that the model weights and the corresponding weighted model means can be sensitive to the weighting strategy that is applied. For instance, when estimating model weights based on Louisiana precipitation, the weighted projections show a wetter and cooler south-central domain in the future compared to other weighting strategies. Alternatively, for example, when estimating model weights based on New Mexico temperature, the weighted projections show a drier and warmer south-central domain in the future. However, when considering the entire south-central domain in estimating the model weights, the weighted future projections show a compromise in the precipitation and temperature estimates. As for uncertainty, our matrix of results provided a more certain picture of future climate compared to the spread in the original model ensemble. If future impact assessments utilize weighting strategies, then our findings suggest that how the specific weighting strategy is used with climate projections may depend on the needs of an impact assessment or adaptation plan.

APA, Harvard, Vancouver, ISO, and other styles

50

Lappi, Juha, and Robert L. Bailey. "Estimation of the Diameter Increment Function or Other Tree Relations Using Angle-Count Samples." Forest Science 33, no. 3 (1987): 725–39. http://dx.doi.org/10.1093/forestscience/33.3.725.

Full text

Abstract:

Abstract A formula is derived for the bias when an angle-count sample is used to estimate the mean of a tree variable that is correlated with the breast height diameter. This bias occurs, for instance, if average increment is estimated with increment cores from an angle-count sample. Estimation of mean increment for a given initial diameter is studied further by assuming that increments are log-normally distributed, in which case the sampling distribution is a mixture of three log-normal distributions. An estimate obtained by weighting observations inversely to the basal area (i.e., with the estimated tree frequency) compares favorably in simulations with a parametric estimate derived from the sampling distribution of diameters. If increments are regressed on the initial diameters, then weighting proportionally to the initial basal area and inversely to the current basal area gives smaller bias and standard deviation of parameter estimates than weighting inversely to the current basal area alone. For. Sci. 33(3):725-739.

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!