Muhammad Waqas, Muhammad Atif Tahir, Rizwan Qureshi · Greater South Information System · 2021
The objective of Multiple-instance learning (MIL) is to learn a mapping function from weakly labeled training data, the training data in MIL is arranged in the form of labeled bags, and every bag holds several instances. The label of the bag depends upon the characteristics of unlabeled instances. This formulation has been used in decision-making applications, such as medical image classification and molecular activity prediction. This data formulation leads to a complex hypothesis, and many existing MIL algorithms are not robust to complex hypothesis space. To deal with this limitation, this paper proposes a Fisher vector-based stacking ensemble design with an instance relevance estimation process, called relevance-based multiple-instance Fisher vector encoding (RMI-FV). The ensemble design builds on top of the instance relevance estimation mechanism. The instance relevancy calculation process employs a Gaussian mixture-based subspace clustering approach, which helps to identify instances with higher relevance to the bag label. The experiments show that the proposed RMI-FV achieves better performance than state-of-the-art MIL approaches.
Cited by 0Open access
Muhammad Waqas, Muhammad Atif Tahir, Rizwan Qureshi · Greater South Information System · 2021
The objective of Multiple-instance learning (MIL) is to learn a mapping function from weakly labeled training data, the training data in MIL is arranged in the form of labeled bags, and every bag holds several instances. The label of the bag depends upon the characteristics of unlabeled instances. This formulation has been used in decision-making applications, such as medical image classification and molecular activity prediction. This data formulation leads to a complex hypothesis, and many existing MIL algorithms are not robust to complex hypothesis space. To deal with this limitation, this paper proposes a Fisher vector-based stacking ensemble design with an instance relevance estimation process, called relevance-based multiple-instance Fisher vector encoding (RMI-FV). The ensemble design builds on top of the instance relevance estimation mechanism. The instance relevancy calculation process employs a Gaussian mixture-based subspace clustering approach, which helps to identify instances with higher relevance to the bag label. The experiments show that the proposed RMI-FV achieves better performance than state-of-the-art MIL approaches.
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J. R. Quinlan · Elsevier eBooks · 1993
Cited by 742
Via Approximate Box, Qingping Tao, Stephen Scott et al. · 2004
The multiple-instance learning (MIL) model has been very successful in application areas such as drug discovery and content-based imageretrieval.
Cited by 0
Miao Zhang, Zhize Wu, Shouhong Wan et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
Multiple-instance learning (MIL) has been successfully utilized in image retrieval. Existing approaches cannot select positive instances correctly from positive bags which may result in a low accuracy. In this paper, we propose a new image retrieval approach called multiple instance learning based on instance-consistency (MILIC) to mitigate such issue. First, we select potential positive instances effectively in each positive bag by ranking instance-consistency (IC) values of instances. Then, we design a feature representation scheme, which can represent the relationship among bags and instances, based on potential positive instances to convert a bag into a single instance. Finally, we can use a standard single-instance learning strategy, such as the support vector machine, for performing object-based image retrieval. Experimental results on two challenging data sets show the effectiveness of our proposal in terms of accuracy and run time.
Cited by 0
Antal van den Bosch, Frank Van Eynde, Ineke Schuurman et al. · Data Archiving and Networked Services (DANS) · 2000
Cited by 0
Peter Waiganjo Wagacha · 2003
Cited by 5
Kaustav Mukherjee, B. E (computer Engineering, Kaustav Mukherjee et al. · 2004
Instance based learning (IBL) algorithms attempt to classify a new unseen instance (test data) based on some “proximal neighbour ” rule, i.e. by taking a majority vote among the class labels of its proximal instances in the training or reference dataset. The k nearest neighbours (k-NN) are commonly used as the proximal neighbours. We study the use of the state of the art approximate technique of k-NN search on the best known IBL algorithms. The results are impressive; substantial speed up in computation is achieved and on average the accuracy of classification is preserved. Geometric proximity graphs especially the Gabriel graph (a subgraph of the well known Delaunay triangulation) provides an elegant algorithmic alternative to k-NN based IBL algorithms. The main reason for this is that the Gabriel graph preserves the original nearest neighbour decision boundary between data points of different classes very well. However computing the Gabriel graph of a dataset in practice is prohibitively expensive. Extending the idea of approximate k-NN search to approximate Gabriel neighbours search, it becomes feasible to compute the latter. We thin (reduce) the original reference
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D.R. Wilson · 1997
The basic nearest-neighbor rule generalizes well in many domains but has several shortcomings, including inappropriate distance functions, large storage requirements, slow execution time, sensitivity to noise, and an inability to adjust its decision boundaries after storing the training data. This paper proposes methods for overcoming each of these weaknesses and combines these methods into a comprehensive learning system called the Integrated Decremental Instance-Based Learning Algorithm (IDIBL) that seeks to reduce storage, improve execution speed, and increase generalization accuracy, when compared to the basic nearest neighbor algorithm and other learning models. IDIBL tunes its own parameters using a new measure of fitness that combines confidence and cross-validation (CVC) accuracy in order to avoid discretization problems with more traditional leave-one-out cross-validation (LCV). In our experiments IDIBL achieves higher generalization accuracy than other less comprehensive instance-based learning algorithms, while requiring less than onefourth the storage of the nearest neighbor algorithm and improving execution speed by a corresponding factor. In experiments on 21 datasets, IDIBL also achieves higher generalization accuracy than those reported for 16 major machine learning and neural network models.
Cited by 21
Rhythm Girdhar, Priya S. Nayak, Shreekanth M. Prabhu · Lecture notes on data engineering and communications technologies · 2022
Cited by 1
Cleotilde González, Varun Dutt · PsycEXTRA Dataset · 2010
Cited by 3
Maskey, M. · Data Archiving and Networked Services (DANS) · 2004
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Ashutosh Pandey, Bradley Schmerl, David Garlan · 2017
Due to the fundamental trade-off between quality and timeliness of planning, designers of self-adaptive systems often have to compromise between an approach that is quick to find an adaptation plan and an approach that is slow but finds a quality adaptation plan. To deal with this trade-off, in our previous work, we proposed a hybrid planning approach that combines a deliberative and a reactive planning approach to find a balance between quality and timeliness of planning. However, when reactive and deliberative planning is combined to instantiate a hybrid planner, the key challenge is to decide which approach(es) should be invoked to solve a planning problem. To this end, this paper proposes to use a data-driven instance-based learning to find an appropriate combination of the two planning approaches when solving a planning problem. As an initial proof of concept, the paper presents results of a small experiment that indicate the potential of the proposed approach to identify a combination of the two planning approaches to solve a planning problem.
Cited by 2
Serendero Sáez, Santiago Patricio, Toro Bonilla, Miguel · idUS (Universidad de Sevilla) · 2023
This paper introduces a new classification algorithm of the instance-based learning type. Training records are converted into patterns associated with a known class label, and stored permanently into a trie1-like tree structure along with other helpful information. Classifying new records is done selecting from the trie two best patterns as solutions hypotheses. Best pattern selection is done using standard distance metrics, a strength function and an exclusive values concept. Classification tests done on several data files have shown very accurate results.
Cited by 0Open access
Patricio Serendero, Miguel Toro · 2001
Abstract: This paper introduces a new classification algorithm of the instance-based learning type. Training records are converted into patterns associated with a known class label, and stored permanently into a trie 1-like tree structure along with other helpful information. Classifying new records is done selecting from the trie two best patterns as solutions hypotheses. Best pattern selection is done using standard distance metrics, a strength function and an exclusive values concept. Classification tests done on several data files have shown very accurate results.
Cited by 0
Anh T. Pham, Raviv Raich · 2014
Multi instance multi label learning is a framework in which objects are represented as bags of instances and labels are provided at the bag level. Instance annotation is the problem of assigning labels to the instances in a bag given only the bag label. Recently, OR-ed logistic regression (OR-LR) model and an EM based inference method have been proposed for instance annotation. Due to the linear nature of the logistic regression function, OR-LR performance on linearly inseparable data is limited. This paper addresses this problem by proposing a regularized kernel-based extension to the OR-LR framework. Experiments show that the kernel-based OR-LR algorithm achieves a significant improvement in classification accuracy over the linear OR-LR from 3% to 9% on audio bird song and image annotation datasets and two synthetic datasets.
Cited by 1
Xingqi Wang, Dan Wei, Hui Cheng et al. · Neurocomputing · 2016
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Xiaoguang Li, Wanting Ji, Jidong Huang · Engineering Applications of Artificial Intelligence · 2024
Cited by 8
HAN-GIL MOON, JAE-MO YANG, WON-GOOK CHOI et al. · OSF Preprints (OSF Preprints) · 2022
In acoustic scene classification (ASC) task, an acoustic scene consists of diverse attributes and is inferred by identifying combinations of some distinct attributes among them. This study aims to extract and cluster these attributes effectively using a multiple-instance learning (MIL) framework for ASC. MIL, known as one of the weakly supervised learning methods, is a way to extract instances from input data and infer a scene corresponding to the input data with those unlabeled instances. We develop the MIL framework more suitable for ASC systems, adopting instance-level labels and instance-level loss, which are effective in extracting and clustering instances. As a result, the witness rate increases significantly compared to the framework without instance-level loss and labels. Also in several MIL-based ASC systems, the classification accuracy improves by about 5 to 11\% than without instance-level loss. In addition, we designed a fully separated convolutional module which is a low-complexity neural network consisting of pointwise, frequency-sided depthwise, and temporal-sided depthwise convolutional filters. Considering both complexity and performance, our proposed system is more practical compared to previous systems on the DCASE 2019 challenge task 1-A leader board. We surpassed the third-place model by achieving a performance of 82.3\% with only the model complexity of 417K, which is at least 40 times fewer than other systems.
Cited by 2Open access
Aida de Haro-García, Javier Pérez-Rodríguez, Nicolás García‐Pedrajas · Swarm and Evolutionary Computation · 2018
Cited by 23