A review of multi-instance learning assumptions

Foulds, James; Frank, Eibe

doi:10.1017/s026988890999035x

Cited by 329 publications

(252 citation statements)

References 34 publications

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“…The MIL task is to build a model based on the given images (bags) and predict the class labels of future images (bags) [17]. Formally, let χ denote the bag space and γ be the set of class labels.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The standard assumption of MIL is that the positive bag has at least one positive instance and the negative bag has no positive instance [17]. The generalized multiple-instance learning (GMIL) was proposed.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Labels are only provided for the training bags, and the labels of instances are unknown. The MIL task is to learn a model to classify new bags [16,17].…”

Section: Introductionmentioning

confidence: 99%

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Breast Ultrasound Image Classification Based on Multiple-Instance Learning

et al. 2012

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Breast ultrasound (BUS) image segmentation is a very difficult task due to poor image quality and speckle noise. In this paper, local features extracted from roughly segmented regions of interest (ROIs) are used to describe breast tumors. The roughly segmented ROI is viewed as a bag. And subregions of the ROI are considered as the instances of the bag. Multiple-instance learning (MIL) method is more suitable for classifying breast tumors using BUS images. However, due to the complexity of BUS images, traditional MIL method is not applicable. In this paper, a novel MIL method is proposed for solving such task. First, a self-organizing map is used to map the instance space to the concept space. Then, we use the distribution of the instances of each bag in the concept space to construct the bag feature vector. Finally, a support vector machine is employed for classifying the tumors. The experimental results show that the proposed method can achieve better performance: the accuracy is 0.9107 and the area under receiver operator characteristic curve is 0.96 (p<0.005).

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

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Breast Ultrasound Image Classification Based on Multiple-Instance Learning

et al. 2012

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“…Multi-instance single-label learning, also called multiinstance learning [10,11], can be viewed as a degenerated version of MIML where the most labels associated with a bag are neglected and only one label is concerned. Indeed, multiinstance single-label learning has been exploited as a bridge in degeneration-based MIML algorithms such as MimlBoost [1,2].…”

Section: Preliminariesmentioning

confidence: 99%

Learnability of multi-instance multi-label learning

Wang

Zhou

2012

Chin. Sci. Bull.

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Multi-instance multi-label learning (MIML) is a new machine learning framework where one data object is described by multiple instances and associated with multiple class labels. During the past few years, many MIML algorithms have been developed and many applications have been described. However, there lacks theoretical exploration to the learnability of MIML. In this paper, through proving a generalization bound for multi-instance single-label learner and viewing MIML as a number of multi-instance single-label learning subtasks with the correlation among the labels, we show that the MIML hypothesis class constructed from a multi-instance single-label hypothesis class is PAC-learnable. Multi-instance multi-label learning (MIML) [1, 2] is a new machine learning framework. In contrast to traditional supervised learning where one data object is represented by one instance and associated with one class label, in MIML one object is described by multiple instances and associated with multiple class labels (Figure 1). Such a framework is particularly useful for handling complicated data objects with multiple semantic meanings. For example, in image annotation, an image contains many patches each can be represented by an instance, while the image can be assigned with multiple annotation terms simultaneously; in text categorization, one document contains multiple sections each can be represented by an instance, while the document can be classified into multiple categories simultaneously.Formally, let X and Y denote the instance space and the set of class labels, respectively. The task of MIML is to learn a function f : 2 X → 2 Y from a given data setis a set of labels {y i1 , y i2 , · · · , y i,l }. n i denotes the number of instances in X i , l denotes the number of candidate labels and y ik ∈ {−1, +1} (k = 1, 2, · · · , l). X i is also called as a bag (of instances) and let D denote the distribution over the bags.

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“…Multiple instance learning (MIL) is a generalization of traditional supervised learning having growing interest [6,12,16,39]. Unlike traditional learning, in multi-instance learning, an example is called a bag and it represents a set of non-repeated instances.…”

Section: Introductionmentioning

confidence: 99%

Speeding up multiple instance learning classification rules on GPUs

2014

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Multiple instance learning is a challenging task in supervised learning and data mining. However, algorithm performance becomes slow when learning from large-scale and high-dimensional data sets. Graphics processing units (GPUs) are being used for reducing computing time of algorithms. This paper presents an implementation of the G3P-MI algorithm on GPUs for solving multiple instance problems using classification rules. The GPU model proposed is distributable to multiple GPUs, seeking for its scalability across large-scale and high-dimensional data sets. The proposal is compared to the multi-threaded CPU algorithm with SSE parallelism over a series of data sets. Experimental results report that the computation time can be significantly reduced and its scalability improved. Specifically, an speedup of up to 149× can be achieved over the multi-threaded CPU algorithm when using four GPUs, and the rules interpreter achieves great efficiency and runs over 108 billion Genetic Programming operations per second.

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A review of multi-instance learning assumptions

Cited by 329 publications

References 34 publications

Breast Ultrasound Image Classification Based on Multiple-Instance Learning

Breast Ultrasound Image Classification Based on Multiple-Instance Learning

Learnability of multi-instance multi-label learning

Speeding up multiple instance learning classification rules on GPUs

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