Efficient sequential feature selection based on adaptive eigenspace model

Gu, Nannan; Fan, Mingyu; Du, Liang; Ren, Dongchun

doi:10.1016/j.neucom.2015.02.043

Cited by 36 publications

(12 citation statements)

References 31 publications

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“…labeling adaptation and instance adaptation. In addition, some effective methods for feature selection problems have been proposed, such as improved Fisher score algorithm [87] and enhanced bare-bones particle swarm optimization (BPSO) [88]. Moreover, for some specific problems, such as unreliable data [89], [90], incomplete data [91]- [94], text data [95], and costly data [96], researchers have also proposed the corresponding feature selection methods.…”

Section: Variants and Extensions Of Feature Selection A Hybrid mentioning

confidence: 99%

“…a small number of parameters to be tuned that are easy to implement and independent of the gradient of an optimization objective, more and more studies have been focused on utilizing these heuristic algorithms to deal with feature selection problems. Representative heuristic algorithms include genetic algorithms [68], [81], [97] [98], differential evolutional algorithms [99], [100], simulated annealing [14], particle swarm optimization [101]- [103], tabu search [104]- [106],and Fisher score algorithms [87], etc. These methods can generally achieve a good feature subset with a fast speed, making the study of feature selection incorporated with search strategies a new trend.…”

Section: B Feature Selection Based On Heuristic Algorithmsmentioning

confidence: 99%

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Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

2019

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Feature selection has been an important research area in data mining, which chooses a subset of relevant features for use in the model building. This paper aims to provide an overview of feature selection methods for big data mining. First, it discusses the current challenges and difficulties faced when mining valuable information from big data. A comprehensive review of existing feature selection methods in big data is then presented. Herein, we approach the review from two aspects: methods specific to a particular kind of big data with certain characteristics and applications of methods in classification analysis, which are significantly different to the existing review work. This paper also highlights the current issues of feature selection in big data and suggests the future research directions. INDEX TERMS Feature selection, big data, data mining, applications.

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Section: Variants and Extensions Of Feature Selection A Hybrid mentioning

confidence: 99%

Section: B Feature Selection Based On Heuristic Algorithmsmentioning

confidence: 99%

Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

2019

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“…Starting by measuring performance on the original (unchanged) dataset, it proceeds by measuring classification performance by using classifiers that are induced in the datasets in which a single feature is omitted. Finally, the least significant feature is detected as the one that caused the lowest drop or highest gain in classifier performance [30]. This feature is afterwards omitted from the dataset, and the procedure is recursively repeated until the minimal required number of features remains or a certain stopping criterion is reached.…”

Section: No Function (A)mentioning

confidence: 99%

Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles

Zhang

Liu

et al. 2019

Sensors

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In the scenario where autonomous underwater vehicles (AUVs) carry out tasks, it is necessary to reliably estimate underwater-moving-target positioning. While cameras often give low-precision visibility in a limited field of view, the forward-looking sonar is still an attractive method for underwater sensing, which is especially effective for long-range tracking. This paper describes an online processing framework based on forward-looking-sonar (FLS) images, and presents a novel tracking approach based on a Gaussian particle filter (GPF) to resolve persistent multiple-target tracking in cluttered environments. First, the character of acoustic-vision images is considered, and methods of median filtering and region-growing segmentation were modified to improve image-processing results. Second, a generalized regression neural network was adopted to evaluate multiple features of target regions, and a representation of feature subsets was created to improve tracking performance. Thus, an adaptive fusion strategy is introduced to integrate feature cues into the observation model, and the complete procedure of underwater target tracking based on GPF is displayed. Results obtained on a real acoustic-vision AUV platform during sea trials are shown and discussed. These showed that the proposed method is feasible and effective in tracking targets in complex underwater environments.

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“…Other abnormalities, such as mastitis, adenopathy, and granuloma, may also be found in breast images [4]. Machine learning (ML) techniques have found its wide applications in many fields such as prediction problems in educational field [5]- [9], bankruptcy prediction [10]- [16], pattern recognition [17]- [28], image editing [29]- [39], feature reduction [40]- [44], fault diagnosis [45]- [50], face recognition and micro-expression recognition [51]- [57], natural language processing [58], [59] and medical diagnosis [60]- [74]. Especially, it has found its great potential in BC diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique

et al. 2021

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Breast cancer (BC) is one of the primary causes of cancer death among women. Early detection of BC allows patients to receive appropriate treatment, thus increasing the possibility of survival. In this work, a new deep-learning (DL) model based on the transfer-learning (TL) technique is developed to efficiently assist in the automatic detection and diagnosis of the BC suspected area based on two techniques namely 80-20 and cross-validation. DL architectures are modeled to be problem-specific. TL uses the knowledge gained during solving one problem in another relevant problem. In the proposed model, the features are extracted from the mammographic image analysis-society (MIAS) dataset using a pretrained convolutional neural network (CNN) architecture such as Inception V3, ResNet50, Visual Geometry Group networks (VGG)-19, VGG-16, and Inception-V2 ResNet. Six evaluation metrics for evaluating the performance of the proposed model in terms of accuracy, sensitivity, specificity, precision, F-score, and area under the ROC curve (AUC) has been chosen. Experimental results show that the TL of the VGG16 model is powerful for BC diagnosis by classifying the mammogram breast images with overall

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Efficient sequential feature selection based on adaptive eigenspace model

Cited by 36 publications

References 31 publications

Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles

A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique

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