Using Similarity between Paired Instances to Improve Multiple-Instance Learning via Embedded Instance Selection

Zhang, Duzhou; Cao, Xibin

doi:10.1007/978-3-642-42042-9_44

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…Several regular machine learning algorithms were adapted to process raw multi‐instance data: maximum likelihood‐based methods, 56–59 decision rules and tree‐based methods, 60–63 SVM‐based methods, 48 and evolutionary‐based methods 64 …”

Section: Multi‐instance Learning Algorithmsmentioning

confidence: 99%

“…If any instance of a given bag is closer to the prototype instance than a threshold, the bag is classified as positive. Expectation–Maximization Diverse Density (EM‐DD) 57 uses the EM algorithm to locate the prototype instances more efficiently. There exist several other MI algorithms based on the Diverse Density approach, such as DD‐SVM 65 and MILES 66 …”

Section: Multi‐instance Learning Algorithmsmentioning

confidence: 99%

“…Instance‐based algorithms rely on some process, which determines the labels of instances in a bag. Provided by such algorithms 56,57,66,86–89 instance labels are aggregated to derive bag labels. In these algorithms, the key instances can be naturally identified by considering assigned instance labels.…”

Section: Multi‐instance Learning Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Chemical complexity challenge: Is multi‐instance machine learning a solution?

Zankov,

Madzhidov,

Varnek

et al. 2023

WIREs Comput Mol Sci

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Molecules are complex dynamic objects that can exist in different molecular forms (conformations, tautomers, stereoisomers, protonation states, etc.) and often it is not known which molecular form is responsible for observed physicochemical and biological properties of a given molecule. This raises the problem of the selection of the correct molecular form for machine learning modeling of target properties. The same problem is common to biological molecules (RNA, DNA, proteins)—long sequences where only key segments, which often cannot be located precisely, are involved in biological functions. Multi‐instance machine learning (MIL) is an efficient approach for solving problems where objects under study cannot be uniquely represented by a single instance, but rather by a set of multiple alternative instances. Multi‐instance learning was formalized in 1997 and motivated by the problem of conformation selection in drug activity prediction tasks. Since then MIL has found a lot of applications in various domains, such as information retrieval, computer vision, signal processing, bankruptcy prediction, and so on. In the given review we describe the MIL framework and its applications to the tasks associated with ambiguity in the representation of small and biological molecules in chemoinformatics and bioinformatics. We have collected examples that demonstrate the advantages of MIL over the traditional single‐instance learning (SIL) approach. Special attention was paid to the ability of MIL models to identify key instances responsible for a modeling property.This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

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Section: Multi‐instance Learning Algorithmsmentioning

confidence: 99%

Section: Multi‐instance Learning Algorithmsmentioning

confidence: 99%

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Chemical complexity challenge: Is multi‐instance machine learning a solution?

Zankov,

Madzhidov,

Varnek

et al. 2023

WIREs Comput Mol Sci

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show abstract

“…Diverse Density (DD) seeks to get the concept point t in the determined feature space (Zhang and Goldman 2001). The concept point t is required to be close to at least one instance from each positive bag and meanwhile it should be far away from instances in negative bags.…”

Section: Em-ddmentioning

confidence: 99%

DCT-MIL: Deep CNN transferred multiple instance learning for COPD identification using CT images

Xia

et al. 2020

Phys. Med. Biol.

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While many pre-defined computed tomographic (CT) measures have been utilized to characterize chronic obstructive pulmonary disease (COPD), it is still challenging to represent pathological alternations of multiple dimensions and highly spatial heterogeneity. Deep CNN transferred multiple instance learning (DCT-MIL) is proposed to identify COPD via CT images. After the lung is divided into eight sections along the axial direction, one random axial CT image is taken out from each section as one instance. With one instance as the input, the activations of neural layers of AlexNet trained by natural images are extracted as features. After dimension reduction through principle component analysis, features of all instances are input into three MIL methods: Citation k-Nearest-Neighbor (Citation-KNN), multiple instance support vector machine, and expectation-maximization diverse density. Moreover, the performance dependence of the resulted models on the depth of the neural layer where activations are extracted and the number of features is investigated. The proposed DCT-MIL achieves an exceptional performance with an accuracy of 99.29% and area under curve of 0.9826 while using 100 principle components of features extracted from the fourth convolutional layer and Citation-KNN. It outperforms not only DCT-MIL models using other settings and the pre-trained AlexNet with fine-tuning by montages of eight lung CT images, but also other state-of-art methods. Deep CNN transferred multiple instance learning is suited for identification of COPD using CT images. It can help finding subgroups with high risk of COPD from large populations through CT scans ordered doing lung cancer screening.

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Using Similarity between Paired Instances to Improve Multiple-Instance Learning via Embedded Instance Selection

Cited by 2 publications

References 8 publications

Chemical complexity challenge: Is multi‐instance machine learning a solution?

Chemical complexity challenge: Is multi‐instance machine learning a solution?

DCT-MIL: Deep CNN transferred multiple instance learning for COPD identification using CT images

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