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Journal articles on the topic 'Instance'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Cheng, An-Chieh, Chieh Hubert Lin, Da-Cheng Juan, Wei Wei, and Min Sun. "InstaNAS: Instance-Aware Neural Architecture Search." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3577–84. http://dx.doi.org/10.1609/aaai.v34i04.5764.

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Conventional Neural Architecture Search (NAS) aims at finding a single architecture that achieves the best performance, which usually optimizes task related learning objectives such as accuracy. However, a single architecture may not be representative enough for the whole dataset with high diversity and variety. Intuitively, electing domain-expert architectures that are proficient in domain-specific features can further benefit architecture related objectives such as latency. In this paper, we propose InstaNAS—an instance-aware NAS framework—that employs a controller trained to search for a “d

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2

Smith-Miles, Kate, and Simon Bowly. "Generating new test instances by evolving in instance space." Computers & Operations Research 63 (November 2015): 102–13. http://dx.doi.org/10.1016/j.cor.2015.04.022.

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Zhao, Fei, Yang Xin, Kai Zhang, and Xinxin Niu. "Representativeness-Based Instance Selection for Intrusion Detection." Security and Communication Networks 2021 (March 12, 2021): 1–13. http://dx.doi.org/10.1155/2021/6638134.

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With the continuous development of network technology, an intrusion detection system needs to face detection efficiency and storage requirement when dealing with large data. A reasonable way of alleviating this problem is instance selection, which can reduce the storage space and improve intrusion detection efficiency by selecting representative instances. An instance is representative not only in its class but also in different classes. This representativeness reflects the importance of an instance. Since the existing instance selection algorithm does not take into account the above situation

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Wang, Hua, Feiping Nie, and Heng Huang. "Learning Instance Specific Distance for Multi-Instance Classification." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 507–12. http://dx.doi.org/10.1609/aaai.v25i1.7893.

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Multi-Instance Learning (MIL) deals with problems where each training example is a bag, and each bag contains a set of instances. Multi-instance representation is useful in many real world applications, because it is able to capture more structural information than traditional flat single-instance representation. However, it also brings new challenges. Specifically, the distance between data objects in MIL is a set-to-set distance, which is harder to estimate than vector distances used in single-instance data. Moreover, because in MIL labels are assigned to bags instead of instances, although

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Ren, Lingyu, Youlong Yang, Liqin Sun, and Xu Wu. "Grey-based multiple instance learning with multiple bag-representative." AI Communications 33, no. 2 (2020): 59–73. http://dx.doi.org/10.3233/aic-200628.

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Multiple instance learning is a modification in supervised learning that handles the classification of collection instances, which called bags. Each bag contains a number of instances whose features are extracted. In multiple instance learning, the standard assumption is that a positive bag contains at least one positive instance, whereas a negative bag is only comprised of negative instances. The complexity of multiple instance learning relies heavily on the number of instances in the training datasets. Since we are usually confronted with a large instance space, it is important to design eff

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Tanaka-Ishii, Kumiko. "An Instance vs. The Instance." Minds and Machines 19, no. 1 (2008): 117–28. http://dx.doi.org/10.1007/s11023-008-9128-0.

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Analyti, Anastasia, Nicolas Spyratos, and Panos Constantopoulos. "Deriving and Retrieving Contextual Categorical Information through Instance Inheritance." Fundamenta Informaticae 44, no. 4 (2000): 321–51. https://doi.org/10.3233/fun-2000-44401.

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In semantic and object-oriented data models, each class has one or more typing properties that associate it to other classes, and carry type information about all instances of the class. We introduce a new kind of property that we call instance-typing property. An instance-typing property associates an instance of a class to another class, and carries type information about that particular instance (and not about all instances of the class). Instance-typing properties are important as they allow to represent summary information about an instance, in addition to specific information. In this pa

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SCOTT, STEPHEN, JUN ZHANG, and JOSHUA BROWN. "ON GENERALIZED MULTIPLE-INSTANCE LEARNING." International Journal of Computational Intelligence and Applications 05, no. 01 (2005): 21–35. http://dx.doi.org/10.1142/s1469026805001453.

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We describe a generalisation of the multiple-instance learning model in which a bag's label is not based on a single instance's proximity to a single target point. Rather, a bag is positive if and only if it contains a collection of instances, each near one of a set of target points. We then adapt a learning-theoretic algorithm for learning in this model and present empirical results on data from robot vision, content-based image retrieval, and protein sequence identification.

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Qurratulain, Safder, Zezhong Zheng, Jun Xia, Yi Ma, and Fangrong Zhou. "Deep learning instance segmentation framework for burnt area instances characterization." International Journal of Applied Earth Observation and Geoinformation 116 (February 2023): 103146. http://dx.doi.org/10.1016/j.jag.2022.103146.

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Gong, Yiping, Fan Zhang, Xiangyang Jia, Zhu Mao, Xianfeng Huang, and Deren Li. "Instance Segmentation in Very High Resolution Remote Sensing Imagery Based on Hard-to-Segment Instance Learning and Boundary Shape Analysis." Remote Sensing 14, no. 1 (2021): 23. http://dx.doi.org/10.3390/rs14010023.

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Although great success has been achieved in instance segmentation, accurate segmentation of instances remains difficult, especially at object edges. This problem is more prominent for instance segmentation in remote sensing imagery due to the diverse scales, variable illumination, smaller objects, and complex backgrounds. We find that most current instance segmentation networks do not consider the segmentation difficulty of different instances and different regions within the instance. In this paper, we study this problem and propose an ensemble method to segment instances from remote sensing

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Xv, Jiabin, and Fei Deng. "3D Point Cloud Instance Segmentation Considering Global Shape Contour Constraints." Remote Sensing 15, no. 20 (2023): 4939. http://dx.doi.org/10.3390/rs15204939.

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Aiming to solve the problem that spatially distributed similar instances cannot be distinguished in 3D point cloud instance segmentation, a 3D point cloud instance segmentation network, considering the global shape contour, was proposed. This research used the global-to-local design idea and added the global shape constraint to solve this problem. A Transformer module (Global Shape Attention, GSA) that can capture the shape contour information of the instance in the scene was designed. This module encoded the shape contour information into the Transformer structure as a Key-Value and extracted

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Yuan, Liming, Jiafeng Liu, and Xianglong Tang. "Multiple-instance learning with pairwise instance similarity." International Journal of Applied Mathematics and Computer Science 24, no. 3 (2014): 567–77. http://dx.doi.org/10.2478/amcs-2014-0041.

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Abstract Multiple-Instance Learning (MIL) has attracted much attention of the machine learning community in recent years and many real-world applications have been successfully formulated as MIL problems. Over the past few years, several Instance Selection-based MIL (ISMIL) algorithms have been presented by using the concept of the embedding space. Although they delivered very promising performance, they often require long computation times for instance selection, leading to a low efficiency of the whole learning process. In this paper, we propose a simple and efficient ISMIL algorithm based o

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Luo, Yixin, Jiaming Han, Zhou Liu, Mi Wang, and Gui-Song Xia. "An Elliptic Centerness for Object Instance Segmentation in Aerial Images." Journal of Remote Sensing 2022 (June 2, 2022): 1–14. http://dx.doi.org/10.34133/2022/9809505.

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Instance segmentation in aerial images is an important and challenging task. Most of the existing methods have adapted instance segmentation algorithms developed for natural images to aerial images. However, these methods easily suffer from performance degradation in aerial images, due to the scale variations, large aspect ratios, and arbitrary orientations of instances caused by the bird’s-eye view of aerial images. To address this issue, we propose an elliptic centerness (EC) for instance segmentation in aerial images, which can assign the proper centerness values to the intricate aerial ins

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Jhuo, I.-Hong, and D. Lee. "Multiple-Instance Learning: Multiple Feature Selection on Instance Representation." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1794–95. http://dx.doi.org/10.1609/aaai.v25i1.8030.

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In multiple-Instance Learning (MIL), training class labels are attached to sets of bags composed of unlabeled instances, and the goal is to deal with classification of bags. Most previous MIL algorithms, which tackle classification problems, consider each instance as a represented feature. Although the algorithms work well in some prediction problems, considering diverse features to represent an instance may provide more significant information for learning task. Moreover, since each instance may be mapped into diverse feature spaces, encountering a large number of irrelevant or redundant feat

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Zhang, Yongjun, Wangshan Yang, Xinyi Liu, Yi Wan, Xianzhang Zhu, and Yuhui Tan. "Unsupervised Building Instance Segmentation of Airborne LiDAR Point Clouds for Parallel Reconstruction Analysis." Remote Sensing 13, no. 6 (2021): 1136. http://dx.doi.org/10.3390/rs13061136.

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Efficient building instance segmentation is necessary for many applications such as parallel reconstruction, management and analysis. However, most of the existing instance segmentation methods still suffer from low completeness, low correctness and low quality for building instance segmentation, which are especially obvious for complex building scenes. This paper proposes a novel unsupervised building instance segmentation (UBIS) method of airborne Light Detection and Ranging (LiDAR) point clouds for parallel reconstruction analysis, which combines a clustering algorithm and a novel model con

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Wanodya, Ayuning, Diana Anggraeni, and Bob Morison Sigalingging. "Translation Method of Emotional Expression in Daniel Keyes’ The Minds of Billy Milligan." Journal of Language and Literature 24, no. 2 (2024): 439–54. http://dx.doi.org/10.24071/joll.v24i2.8017.

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This study examines the English to Indonesian translation of emotional expressions depicted in Daniel Keyes’s novel “The Minds of Billy Milligan” and its Indonesian translation “24 Wajah Billy.” The primary objectives are to describe the emotional expressions portrayed by the main character and to analyze the methods applied in translating these emotional expressions from English to Indonesian. Employing David Krech’s emotional classification theory and drawing on Vinay and Darbelnet’s translation methods, this qualitative descriptive research identifies 28 emotional classification data in the

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Crammer, Koby, and Yoram Singer. "Online Ranking by Projecting." Neural Computation 17, no. 1 (2005): 145–75. http://dx.doi.org/10.1162/0899766052530848.

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We discuss the problem of ranking instances. In our framework, each instance is associated with a rank or a rating, which is an integer in 1 to k. Our goal is to find a rank-prediction rule that assigns each instance a rank that is as close as possible to the instance's true rank. We discuss a group of closely related online algorithms, analyze their performance in the mistake-bound model, and prove their correctness. We describe two sets of experiments, with synthetic data and with the Each Movie data set for collaborative filtering. In the experiments we performed, our algorithms outperform

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Zhouyu Fu, A. Robles-Kelly, and Jun Zhou. "MILIS: Multiple Instance Learning with Instance Selection." IEEE Transactions on Pattern Analysis and Machine Intelligence 33, no. 5 (2011): 958–77. http://dx.doi.org/10.1109/tpami.2010.155.

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Schopman, Balthasar, Shenghui Wang, Antoine Isaac, and Stefan Schlobach. "Instance-Based Ontology Matching by Instance Enrichment." Journal on Data Semantics 1, no. 4 (2012): 219–36. http://dx.doi.org/10.1007/s13740-012-0011-z.

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Pereira, André, Marcus Ritt, and Luciana Buriol. "Finding Optimal Solutions to Sokoban Using Instance Dependent Pattern Databases." Proceedings of the International Symposium on Combinatorial Search 4, no. 1 (2021): 141–48. http://dx.doi.org/10.1609/socs.v4i1.18290.

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Pattern databases have been successfully applied to several problems. Their use assumes that the goal state is known, and once the pattern database is built, commonly it can be used by all instances. However, in Sokoban, before solving the puzzle, the goal position of each stone is unknown. Moreover, each Sokoban instance has its own state space search. In this paper we apply pattern databases to Sokoban. The proposed approach uses an instance decomposition, that allows multiple possible goal states to be abstracted into a single state. Thus, an instance dependent pattern database is employed.

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21

Yuan, Liming, Jiafeng Liu, Xianglong Tang, Daming Shi, and Lu Zhao. "Pairwise-similarity-based instance reduction for efficient instance selection in multiple-instance learning." International Journal of Machine Learning and Cybernetics 6, no. 1 (2014): 83–93. http://dx.doi.org/10.1007/s13042-014-0248-y.

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22

Xu, Xinzheng, Qiaoyu Guo, Zhongnian Li, and Dechun Li. "Uncertainty Ordinal Multi-Instance Learning for Breast Cancer Diagnosis." Healthcare 10, no. 11 (2022): 2300. http://dx.doi.org/10.3390/healthcare10112300.

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Ordinal multi-instance learning (OMIL) deals with the weak supervision scenario wherein instances in each training bag are not only multi-class but also have rank order relationships between classes, such as breast cancer, which has become one of the most frequent diseases in women. Most of the existing work has generally been to classify the region of interest (mass or microcalcification) on the mammogram as either benign or malignant, while ignoring the normal mammogram classification. Early screening for breast disease is particularly important for further diagnosis. Since early benign lesi

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Ping, Wei, Ye Xu, Kexin Ren, Chi-Hung Chi, and Furao Shen. "Non-I.I.D. Multi-Instance Dimensionality Reduction by Learning a Maximum Bag Margin Subspace." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 551–56. http://dx.doi.org/10.1609/aaai.v24i1.7653.

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Multi-instance learning, as other machine learning tasks, also suffers from the curse of dimensionality. Although dimensionality reduction methods have been investigated for many years, multi-instance dimensionality reduction methods remain untouched. On the other hand, most algorithms in multi- instance framework treat instances in each bag as independently and identically distributed samples, which fails to utilize the structure information conveyed by instances in a bag. In this paper, we propose a multi-instance dimensionality reduction method, which treats instances in each bag as non-i.i

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Jang, Jaeseok, and Hyuk-Yoon Kwon. "TAIL-MIL: Time-Aware and Instance-Learnable Multiple Instance Learning for Multivariate Time Series Anomaly Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 17582–89. https://doi.org/10.1609/aaai.v39i17.33933.

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This study addresses the challenge of detecting anomalies in multivariate time series data. Considering a bag (e.g., multi-sensor data) consisting of two-dimensional spaces of time points and multivariate instances (e.g., individual sensors), we aim to detect anomalies at both the bag and instance level with a unified model. To circumvent the practical difficulties of labeling at the instance level in such spaces, we adopt a multiple instance learning (MIL)-based approach, which enables learning at both the bag- and instance- levels using only the bag-level labels. In this study, we introduce

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Z. Salih, Nibras, and Walaa Khalaf. "ON THE USE OF MULTIPLE INSTANCE LEARNING FOR DATA CLASSIFICATION." Journal of Engineering and Sustainable Development 25, Special (2021): 1–127. http://dx.doi.org/10.31272/jeasd.conf.2.1.15.

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In the multiple instances learning framework, instances are arranged into bags, each bag contains several instances, the labels of each instance are not available but the label is available for each bag. Whilst in a single instance learning each instance is connected with the label that contains a single feature vector. This paper examines the distinction between these paradigms to see if it is appropriate, to cast the problem within a multiple instance framework. In single-instance learning, two datasets are applied (students’ dataset and iris dataset) using Naïve Bayes Classifier (NBC), Mult

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Sun, Yu-Yin, Michael Ng, and Zhi-Hua Zhou. "Multi-Instance Dimensionality Reduction." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 587–92. http://dx.doi.org/10.1609/aaai.v24i1.7700.

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Multi-instance learning deals with problems that treat bags of instances as training examples. In single-instance learning problems, dimensionality reduction is an essential step for high-dimensional data analysis and has been studied for years. The curse of dimensionality also exists in multiinstance learning tasks, yet this difficult task has not been studied before. Direct application of existing single-instance dimensionality reduction objectives to multi-instance learning tasks may not work well since it ignores the characteristic of multi-instance learning that the labels of bags are kno

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Xue, Xingsi, and Jianhua Liu. "A Compact Hybrid Evolutionary Algorithm for Large Scale Instance Matching in Linked Open Data Cloud." International Journal on Artificial Intelligence Tools 26, no. 04 (2017): 1750013. http://dx.doi.org/10.1142/s0218213017500130.

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Establishing correct links among the coreference ontology instances is critical to the success of Linked Open Data (LOD) cloud. However, because of the high level heterogeneity and large scale instance set, matching the coreference instances in LOD cloud is an error prone and time consuming task. To this end, in this work, we present an asymmetrical profile-based similarity measure for instance matching task, construct new optimal models for schema-level and instance-level matching problems, and propose a compact hybrid evolutionary algorithm based ontology matching approach to solve the large

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Ryu, Sehyun, Hosung Joo, Jonggyu Jang, and Hyun Jong Yang. "Instance-Wise Laplace Mechanism via Deep Reinforcement Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23640–41. http://dx.doi.org/10.1609/aaai.v38i21.30506.

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Recent research has shown a growing interest in per-instance differential privacy (pDP), highlighting the fact that each data instance within a dataset may incur distinct levels of privacy loss. However, conventional additive noise mechanisms apply identical noise to all query outputs, thereby deteriorating data statistics. In this study, we propose an instance-wise Laplace mechanism, which adds non-identical Laplace noises to the query output for each data instance. A challenge arises from the complex interaction of additive noise, where the noise introduced to individual instances impacts th

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Huang, Wei, Shiyu Deng, Chang Chen, Xueyang Fu, and Zhiwei Xiong. "Learning to Model Pixel-Embedded Affinity for Homogeneous Instance Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 1007–15. http://dx.doi.org/10.1609/aaai.v36i1.19984.

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Homogeneous instance segmentation aims to identify each instance in an image where all interested instances belong to the same category, such as plant leaves and microscopic cells. Recently, proposal-free methods, which straightforwardly generate instance-aware information to group pixels into different instances, have received increasing attention due to their efficient pipeline. However, they often fail to distinguish adjacent instances due to similar appearances, dense distribution and ambiguous boundaries of instances in homogeneous images. In this paper, we propose a pixel-embedded affini

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Zhao, Wenyue, Yang Cao, Peter Buneman, Jia Li, and Nikos Ntarmos. "Automating Vectorized Distributed Graph Computation." Proceedings of the ACM on Management of Data 2, no. 6 (2024): 1–27. https://doi.org/10.1145/3698833.

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Multi-instance graph algorithms interleave the evaluation of multiple instances of the same algorithm with different inputs over the same graph. They have been shown to be significantly faster than traditional serial and batch evaluation, by sharing computation across instances. However, writing correct multi-instance algorithms is challenging; and in this work, we describe AutoMI, a framework for automatically converting vertex-centric graph algorithms into their vectorized multi-instance versions. We also develop an algebraic characterization of algorithms that can benefit best from multi-in

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Lin, Tiancheng, Hongteng Xu, Canqian Yang, and Yi Xu. "Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1601–9. http://dx.doi.org/10.1609/aaai.v36i2.20051.

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When applying multi-instance learning (MIL) to make predictions for bags of instances, the prediction accuracy of an instance often depends on not only the instance itself but also its context in the corresponding bag. From the viewpoint of causal inference, such bag contextual prior works as a confounder and may result in model robustness and interpretability issues. Focusing on this problem, we propose a novel interventional multi-instance learning (IMIL) framework to achieve deconfounded instance-level prediction. Unlike traditional likelihood-based strategies, we design an Expectation-Maxi

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Gao, Wenjing, Wenjun Zhang, Haiyan Gao, and Yonghua Zhu. "Visual sentiment analysis via deep multiple clustered instance learning." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7217–31. http://dx.doi.org/10.3233/jifs-200675.

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The increasing tendency of people expressing opinions via images online has motivated the development of automatic assessment of sentiment from visual contents. Based on the observation that visual sentiment is conveyed through many visual elements in images, we put forward to tackle visual sentiment analysis under multiple instance learning (MIL) formulation. We propose a deep multiple clustered instance learning formulation, under which a deep multiple clustered instance learning network (DMCILN) is constructed for visual sentiment analysis. Specifically, the input image is converted into a

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Veganzones, Miguel, Ana Cisnal, Eusebio de la Fuente, and Juan Carlos Fraile. "Training Fully Convolutional Neural Networks for Lightweight, Non-Critical Instance Segmentation Applications." Applied Sciences 14, no. 23 (2024): 11357. https://doi.org/10.3390/app142311357.

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Augmented reality applications involving human interaction with virtual objects often rely on segmentation-based hand detection techniques. Semantic segmentation can then be enhanced with instance-specific information to model complex interactions between objects, but extracting such information typically increases the computational load significantly. This study proposes a training strategy that enables conventional semantic segmentation networks to preserve some instance information during inference. This is accomplished by introducing pixel weight maps into the loss calculation, increasing

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Kajino, Hiroshi, Yukino Baba, and Hisashi Kashima. "Instance-Privacy Preserving Crowdsourcing." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 2 (September 5, 2014): 96–103. http://dx.doi.org/10.1609/hcomp.v2i1.13146.

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Crowdsourcing is a technique to outsource tasks to a number of workers. Although crowdsourcing has many advantages, it gives rise to the risk that sensitive information may be leaked, which has limited the spread of its popularity. Task instances (data workers receive to process tasks) often contain sensitive information, which can be extracted by workers. For example, in an audio transcription task, an audio file corresponds to an instance, and the content of the audio (e.g., the abstract of a meeting) can be sensitive information. In this paper, we propose a quantitative analysis framework f

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Injac, Bozana, and Aleksandar Kostic. "The effect of subordinate and superodinate class in categorization task." Psihologija 36, no. 3 (2003): 331–51. http://dx.doi.org/10.2298/psi0303331i.

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Categorization as a function of position of subordinate instance and superordinate class (e.g. cat-mammal vs. mammal-cat) was investigated in six experiments. Participant's task was to answer (by pressing yes/no key) whether an instance (e.g. cat) and a class (e.g. mammal) are categorically congruent. In Experiments 1, 3 an 5 instance was presented as the first stimulus in a pair, while in Experiments 2, 4 and 6 it was the second stimulus in a pair. In Experiments 1 and 2 instance was presented within a blocked design, while class was varied (e.g. mammal/insect), while in Experiments 3 and 4 t

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Morris, J. Garrett, and Mark P. Jones. "Instance chains." ACM SIGPLAN Notices 45, no. 9 (2010): 375–86. http://dx.doi.org/10.1145/1932681.1863596.

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Orponen, Pekka, Ker-i. Ko, Uwe Schöning, and Osamu Watanabe. "Instance complexity." Journal of the ACM 41, no. 1 (1994): 96–121. http://dx.doi.org/10.1145/174644.174648.

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Shang, Chao, Hongliang Li, Fanman Meng, et al. "Instance-level Context Attention Network for instance segmentation." Neurocomputing 472 (February 2022): 124–37. http://dx.doi.org/10.1016/j.neucom.2021.11.104.

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Lin, Yi, and Honggang Zhang. "Regularized Instance Embedding for Deep Multi-Instance Learning." Applied Sciences 10, no. 1 (2019): 64. http://dx.doi.org/10.3390/app10010064.

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In the era of Big Data, multi-instance learning, as a weakly supervised learning framework, has various applications since it is helpful to reduce the cost of the data-labeling process. Due to this weakly supervised setting, learning effective instance representation/embedding is challenging. To address this issue, we propose an instance-embedding regularizer that can boost the performance of both instance- and bag-embedding learning in a unified fashion. Specifically, the crux of the instance-embedding regularizer is to maximize correlation between instance-embedding and underlying instance-l

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Yixin Chen, Jinbo Bi, and J. Z. Wang. "MILES: Multiple-Instance Learning via Embedded Instance Selection." IEEE Transactions on Pattern Analysis and Machine Intelligence 28, no. 12 (2006): 1931–47. http://dx.doi.org/10.1109/tpami.2006.248.

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Briggs, Forrest, Xiaoli Z. Fern, Raviv Raich, and Qi Lou. "Instance Annotation for Multi-Instance Multi-Label Learning." ACM Transactions on Knowledge Discovery from Data 7, no. 3 (2013): 1–30. http://dx.doi.org/10.1145/2513092.2500491.

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Briggs, Forrest, Xiaoli Z. Fern, Raviv Raich, and Qi Lou. "Instance Annotation for Multi-Instance Multi-Label Learning." ACM Transactions on Knowledge Discovery from Data 7, no. 3 (2013): 1–30. http://dx.doi.org/10.1145/2500491.

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AYDIN, Fatih. "Sınıflar Arası Kenar Payını Genişletmek İçin Yeni Bir Örnek Seçim Algoritması." Journal of Intelligent Systems: Theory and Applications 5, no. 2 (2022): 119–26. http://dx.doi.org/10.38016/jista.1033354.

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As discarding superfluous instances in data sets shortens the learning process, it also increases learning performance because of eliminating noisy data. Instance selection methods are commonly utilized to undertake the abovementioned tasks. In this paper, we propose a new supervised instance selection algorithm called Border Instances Reduction using Classes Handily (BIRCH). BIRCH considers k-nearest neighbors of each instance and selects instances that have neighbors from the only same class, namely, but not having neighbors from the different classes. It has been compared with one tradition

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Abbasi, Zeinab, and Mohsen Rahmani. "An Instance Selection Algorithm Based on ReliefF." International Journal on Artificial Intelligence Tools 28, no. 01 (2019): 1950001. http://dx.doi.org/10.1142/s0218213019500015.

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Due to the increasing growth of data, many methods are proposed to extract useful data and remove noisy data. Instance selection is one of these methods which selects some instances of a data set and removes others. This paper proposes a new instance selection algorithm based on ReliefF, which is a feature selection algorithm. In the proposed algorithm, based on the Jaccard index, the nearest instances of each class are found for each instance. Then, based on the nearest neighbor’s set, the weight of each instance is calculated. Finally, only instances with more weights are selected. This algo

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Labowski, Michal, and Piotr Kaniewski. "Multi-Instance Inertial Navigation System for Radar Terrain Imaging." Remote Sensing 12, no. 21 (2020): 3639. http://dx.doi.org/10.3390/rs12213639.

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Navigation systems used for the motion correction (MOCO) of radar terrain images have several limitations, including the maximum duration of the measurement session, the time duration of the synthetic aperture, and only focusing on minimizing long-term positioning errors of the radar host. To overcome these limitations, a novel, multi-instance inertial navigation system (MINS) has been proposed by the authors. In this approach, the classic inertial navigation system (INS), which works from the beginning to the end of the measurement session, was replaced by short INS instances. The initializat

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Wang, Jie, Liangjian Cai, Jinzhu Peng, and Yuheng Jia. "A Novel Multiple Instance Learning Method Based on Extreme Learning Machine." Computational Intelligence and Neuroscience 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/405890.

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Since real-world data sets usually contain large instances, it is meaningful to develop efficient and effective multiple instance learning (MIL) algorithm. As a learning paradigm, MIL is different from traditional supervised learning that handles the classification of bags comprising unlabeled instances. In this paper, a novel efficient method based on extreme learning machine (ELM) is proposed to address MIL problem. First, the most qualified instance is selected in each bag through a single hidden layer feedforward network (SLFN) whose input and output weights are both initialed randomly, an

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Zhu, Hongming, Xiaowen Wang, Yizhi Jiang, Hongfei Fan, Bowen Du, and Qin Liu. "FTRLIM: Distributed Instance Matching Framework for Large-Scale Knowledge Graph Fusion." Entropy 23, no. 5 (2021): 602. http://dx.doi.org/10.3390/e23050602.

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Instance matching is a key task in knowledge graph fusion, and it is critical to improving the efficiency of instance matching, given the increasing scale of knowledge graphs. Blocking algorithms selecting candidate instance pairs for comparison is one of the effective methods to achieve the goal. In this paper, we propose a novel blocking algorithm named MultiObJ, which constructs indexes for instances based on the Ordered Joint of Multiple Objects’ features to limit the number of candidate instance pairs. Based on MultiObJ, we further propose a distributed framework named Follow-the-Regular-

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Li, Jia, Wenyue Zhao, Nikos Ntarmos, Yang Cao, and Peter Buneman. "MITra: A Framework for Multi-Instance Graph Traversal." Proceedings of the VLDB Endowment 16, no. 10 (2023): 2551–64. http://dx.doi.org/10.14778/3603581.3603594.

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This paper presents MITra, a framework for composing multi-instance graph algorithms that traverse from multiple source vertices simultaneously over a single thread. Underlying MITra is a model of multi-instance traversal that uniformly captures traversal sharing across instances. Based on this, MITra provides a programming model that allows users to express traversals by declaring vertex ranks and specify computation logic via an edge function. It synthesizes multi-instance traversal algorithms from declared vertex ranks and edge functions adopted from classic single-instance algorithms, auto

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García-Pedrajas, Nicolás, and Javier Pérez-Rodríguez. "Multi-selection of instances: A straightforward way to improve evolutionary instance selection." Applied Soft Computing 12, no. 11 (2012): 3590–602. http://dx.doi.org/10.1016/j.asoc.2012.06.013.

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Liu, Bo, and Xiao Qi. "A dynamic and adaptive class-balanced data augmentation approach for 3D LiDAR point clouds." PLOS ONE 20, no. 3 (2025): e0318888. https://doi.org/10.1371/journal.pone.0318888.

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3D LiDAR point clouds, obtained through scanning by LiDAR devices, contain rich information such as 3D coordinates (X, Y, Z), color, classification values, intensity values, and time. However, the original collected 3D LiDAR point clouds often exhibit significant disparities in instance counts, which can hinder the effectiveness of point cloud segmentation. PolarMix, a data augmentation algorithm for 3D LiDAR point cloud datasets, addresses this issue by rotating and pasting selected class instances around the Z axis multiple times to enrich the distribution of the point cloud. However, PolarM

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