Speeding up multiple instance learning classification rules on GPUs

Cano, Alberto; Zafra, Amelia; Ventura, Sebastián

doi:10.1007/s10115-014-0752-0

Cited by 19 publications

(6 citation statements)

References 46 publications

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“…Multi-instance learning, also referred to as multi-instance single-label learning, studies the problems in which an object is described by a bag of instances while associated with a single label [14], [15].…”

Section: B Multi-instance Learningmentioning

confidence: 99%

“…In [12], [13], a multi-instance multi-label learning (MIML) framework was proposed for multi-label classification. In MIML, the training samples are represented as bags [14], [15], each of which is described by multiple feature vectors named instances. A bag is labeled positively if at least one of its instances is positive, while it is defined negatively if all instances in it are negative.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Label Remote Sensing Scene Classification Using Multi-Bag Integration

2019

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For remote sensing (RS) scene classification, most of the existing techniques annotate a scene image with merely a single semantic label. However, with the recent advance of remote sensing technology, more abundant information is contained in high-resolution scenes, making a scene image having multiple semantic meanings (i.e., multilabels). Since multi-label RS scene image annotation is a domain full of challenges due to the ambiguities between complicated scene contents and labels, it motivates us to present a novel algorithm which is based on multi-bag integration. First, to describe the semantic content of RS scene image, we propose to partition a scene image into image patches, defined by a regular grid, and extract the heterogeneous features within each. Second, two kinds of image instance bag, namely segmented instance bag (SIB) and layered instance bag (LIB), are designed to represent the scene image. Third, a Mahalanobis distance-based K-Medoids approach is applied to cluster SIB and LIB, respectively, to convert the multi-instance into single-instance, and then the obtained two single-instances are concatenated to generate more powerful scene-aware representation. At last, a multi-class classification technique is used to make predictions on the class labels. Experiments are performed on real remote sensing images and the results show that the proposed method is valid and can achieve superior performance to a number of stateof-the-art approaches.

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Section: B Multi-instance Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Label Remote Sensing Scene Classification Using Multi-Bag Integration

2019

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“…In Ref , they proposed an implementation for Pittsburgh classifiers, which increase the computational complexity by representing an individual as a full classifier (set of rules) rather than individual rules. Extensions of these rule‐based classifiers were proposed for multi‐instance learning . The main advantages of these proposals are their transparent scalability to multiple GPUs, since populations subsets may be assigned easily to different devices without any kind of additional overhead.…”

Section: Data Mining Tasks and Techniquesmentioning

confidence: 99%

A survey on graphic processing unit computing for large‐scale data mining

Cano

2017

WIREs Data Min & Knowl

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General purpose computation using Graphic Processing Units (GPUs) is a wellestablished research area focusing on high-performance computing solutions for massively parallelizable and time-consuming problems. Classical methodologies in machine learning and data mining cannot handle processing of massive and high-speed volumes of information in the context of the big data era. GPUs have successfully improved the scalability of data mining algorithms to address significantly larger dataset sizes in many application areas. The popularization of distributed computing frameworks for big data mining opens up new opportunities for transformative solutions combining GPUs and distributed frameworks. This survey analyzes current trends in the use of GPU computing for large-scale data mining, discusses GPU architecture advantages for handling volume and velocity of data, identifies limitation factors hampering the scalability of the problems, and discusses open issues and future directions.

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“…GPUs are devices with multi-core architectures and massive parallel processor units, which provide fast parallel hardware for a fraction of the cost of a traditional parallel system. Since the introduction of the Computer Unified Device Architecture (CUDA) in 2007, researchers have harnessed the GPU for general purpose computing, and specifically, genetic programming [14,15], and dimensionality reduction [56].…”

Section: Implementation On Gpusmentioning

confidence: 99%

“…This process involves thousands or even millions of threads that collaborate for fast and efficient fitness computation, solving the run-time problem of the evolutionary algorithm. More specific details about the parallel implementation are out of the scope of this paper, and the reader is referred to the articles in [14,15] for GPU implementation details.…”

Section: Implementation On Gpusmentioning

confidence: 99%

Multi-objective genetic programming for feature extraction and data visualization

2015

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Feature extraction transforms high dimensional data into a new subspace of lower dimensionality while keeping the classification accuracy. Traditional algorithms do not consider the multi-objective nature of this task. Data transformations should improve the classification performance on the new subspace, as well as to facilitate data visualization, which has attracted increasing attention in recent years. Moreover, new challenges arising in data mining, such as the need to deal with imbalanced data sets call for new algorithms capable of handling this type of data. This paper presents a Pareto-based multi-objective genetic programming algorithm for feature extraction and data visualization. The algorithm is designed to obtain data transformations that optimize the classification and visualization performance both on balanced and imbalanced data. Six classification and visualization measures are identified as objectives to be optimized by the multi-objective algorithm. The algorithm is evaluated and compared to 11 well-known feature extraction methods, and to the performance on the original high dimensional data. Experimental results on 22 balanced and 20 imbalanced data sets show that it performs very well on both types of data, which is its significant advantage over existing feature extraction algorithms.

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Speeding up multiple instance learning classification rules on GPUs

Cited by 19 publications

References 46 publications

Multi-Label Remote Sensing Scene Classification Using Multi-Bag Integration

Multi-Label Remote Sensing Scene Classification Using Multi-Bag Integration

A survey on graphic processing unit computing for large‐scale data mining

Multi-objective genetic programming for feature extraction and data visualization

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