scholarly journals Brain State Decoding Based on fMRI Using Semisupervised Sparse Representation Classifications

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jing Zhang ◽  
Chuncheng Zhang ◽  
Li Yao ◽  
Xiaojie Zhao ◽  
Zhiying Long
Keyword(s):  
Sparse Representation ◽  
Supervised Learning ◽  
Reconstruction Error ◽  
Classification Performance ◽  
Semisupervised Learning ◽  
Fmri Data ◽  
Training Dataset ◽  
Brain State ◽  
Classification Techniques ◽  
Average Coefficient

Multivariate classification techniques have been widely applied to decode brain states using functional magnetic resonance imaging (fMRI). Due to variabilities in fMRI data and the limitation of the collection of human fMRI data, it is not easy to train an efficient and robust supervised-learning classifier for fMRI data. Among various classification techniques, sparse representation classifier (SRC) exhibits a state-of-the-art classification performance in image classification. However, SRC has rarely been applied to fMRI-based decoding. This study aimed to improve SRC using unlabeled testing samples to allow it to be effectively applied to fMRI-based decoding. We proposed a semisupervised-learning SRC with an average coefficient (semiSRC-AVE) method that performed the classification using the average coefficient of each class instead of the reconstruction error and selectively updated the training dataset using new testing data with high confidence to improve the performance of SRC. Simulated and real fMRI experiments were performed to investigate the feasibility and robustness of semiSRC-AVE. The results of the simulated and real fMRI experiments showed that semiSRC-AVE significantly outperformed supervised learning SRC with an average coefficient (SRC-AVE) method and showed better performance than the other three semisupervised learning methods.

scholarly journals SEMI-SUPERVISED SPARSE REPRESENTATION CLASSIFICATION FOR SLEEP EEG RECOGNITION WITH IMBALANCED SAMPLE SETS

2021 ◽  
pp. 2140006
Author(s):  
XIAOLEI WUZHENG ◽  
SHIGANG ZUO ◽  
LI YAO ◽  
XIAOJIE ZHAO
Keyword(s):  
Sparse Representation ◽  
Supervised Learning ◽  
Class Imbalance ◽  
Classification Performance ◽  
Sleep Eeg ◽  
Sleep Staging ◽  
Sleep Monitoring ◽  
Eeg Data ◽  
Sparse Representation Classification ◽  
Monitoring Devices

Sleep staging with supervised learning requires a large amount of labeled data that are time-consuming and expensive to collect. Semi-supervised learning is widely used to improve classification performance by combining a small amount of labeled data with a large amount of unlabeled data. The accuracy of pseudo-labels in semi-supervised learning may influence the performance of classifier. Based on semi-supervised sparse representation classification, this study proposed an improved sparse concentration index to estimate the confidence of pseudo-labels data for sleep EEG recognition considering both interclass differences and intraclass concentration. In view of class imbalance in sleep EEG data, the synthetic minority oversampling technique was also improved to remove mixed samples at the boundary between minority and majority classes. The results showed that the proposed method achieved better classification performance, in which the classification accuracy after class balancing was obviously higher than that before class balancing. The findings of this study will be beneficial for application in sleep monitoring devices and sleep-related diseases.

Exploring Unclassified Texts Using Multiview Semisupervised Learning

2008 ◽  
pp. 139-161
Author(s):  
Edson Takashi Matsubara ◽  
Maria Carolina Monard ◽  
Ronaldo Cristiano Prati
Keyword(s):  
Text Mining ◽  
Supervised Learning ◽  
Text Classification ◽  
Classification Performance ◽  
Semisupervised Learning ◽  
Experimental Results ◽  
Unlabelled Data

This chapter presents semi-supervised multi-view learning in the context of text mining. Semi-supervised learning uses both labelled and unlabelled data to improve classification performance. It also presents several multi-view semi-supervised algorithms, such as CO-TRAINING, CO-EM, CO-TESTING and CO-EMT, as well as reporting some experimental results using CO-TRAINING in text classification domains. Semi-supervised learning could be very useful whenever there is much more unlabelled than labelled data. This is likely to occur in several text mining applications, where obtaining unlabelled data is inexpensive, although manual labelling the data is

Embedded sparse representation of fMRI data via group-wise dictionary optimization

2016 ◽  
Author(s):  
Dajiang Zhu ◽  
Binbin Lin ◽  
Joshua Faskowitz ◽  
Jieping Ye ◽  
Paul M. Thompson
Keyword(s):  
Sparse Representation ◽  
Fmri Data

scholarly journals Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Wang ◽  
Yanshuang Ren ◽  
Wensheng Zhang
Keyword(s):  
Pearson Correlation ◽  
Brain Regions ◽  
Brain Network ◽  
Classification Performance ◽  
Low Rank ◽  
Fmri Data ◽  
Healthy Controls ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Depression Disorder

Study of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification. One popular approach to construct FBN is Pearson correlation. However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions. Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN. To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls. First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions. Moreover, it can automatically remove weak relationship and retain the modular structure of FBN. Secondly, FBN are effectively measured by eight graph-based features from different aspects. Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN. In addition, the combination of graph-based features in our method further improves classification performance. Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.

Semi-Supervised Classification and its Application to Filtering IDS False Positives

2013 ◽  
Vol 427-429 ◽  
pp. 2309-2312
Author(s):  
Hai Bin Mei ◽  
Ming Hua Zhang
Keyword(s):  
Supervised Learning ◽  
Supervised Classification ◽  
Classification Performance ◽  
False Positives ◽  
Training Data ◽  
Classification Model ◽  
Classification Technique

Alert classifiers built with the supervised classification technique require large amounts of labeled training alerts. Preparing for such training data is very difficult and expensive. Thus accuracy and feasibility of current classifiers are greatly restricted. This paper employs semi-supervised learning to build alert classification model to reduce the number of needed labeled training alerts. Alert context properties are also introduced to improve the classification performance. Experiments have demonstrated the accuracy and feasibility of our approach.

scholarly journals SLCV–a supervised learning—computer vision combined strategy for automated muscle fibre detection in cross-sectional images

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7053
Author(s):  
Anika Rettig ◽  
Tobias Haase ◽  
Alexandr Pletnyov ◽  
Benjamin Kohl ◽  
Wolfgang Ertel ◽  
...  
Keyword(s):  
Computer Vision ◽  
Supervised Learning ◽  
Muscle Fibre ◽  
Cross Sections ◽  
Reconstruction Error ◽  
Muscle Fibres ◽  
Dice Similarity Coefficient ◽  
Cross Sectional ◽  
Staining Methods ◽  
Combined Strategy

Muscle fibre cross-sectional area (CSA) is an important biomedical measure used to determine the structural composition of skeletal muscle, and it is relevant for tackling research questions in many different fields of research. To date, time consuming and tedious manual delineation of muscle fibres is often used to determine the CSA. Few methods are able to automatically detect muscle fibres in muscle fibre cross-sections to quantify CSA due to challenges posed by variation of brightness and noise in the staining images. In this paper, we introduce the supervised learning-computer vision combined pipeline (SLCV), a robust semi-automatic pipeline for muscle fibre detection, which combines supervised learning (SL) with computer vision (CV). SLCV is adaptable to different staining methods and is quickly and intuitively tunable by the user. We are the first to perform an error analysis with respect to cell count and area, based on which we compare SLCV to the best purely CV-based pipeline in order to identify the contribution of SL and CV steps to muscle fibre detection. Our results obtained on 27 fluorescence-stained cross-sectional images of varying staining quality suggest that combining SL and CV performs significantly better than both SL-based and CV-based methods with regards to both the cell separation- and the area reconstruction error. Furthermore, applying SLCV to our test set images yielded fibre detection results of very high quality, with average sensitivity values of 0.93 or higher on different cluster sizes and an average Dice similarity coefficient of 0.9778.

scholarly journals SEMANTIC SEGMENTATION OF BENTHIC COMMUNITIES FROM ORTHO-MOSAIC MAPS

2019 ◽  
Vol XLII-2/W10 ◽  
pp. 151-158 ◽  
Author(s):  
G. Pavoni ◽  
M. Corsini ◽  
M. Callieri ◽  
M. Palma ◽  
R. Scopigno
Keyword(s):  
Visual Analysis ◽  
Marine Organism ◽  
Benthic Communities ◽  
Semantic Segmentation ◽  
Classification Performance ◽  
Training Dataset ◽  
Non Invasive ◽  
Visual Sampling ◽  
Organism Identification

<p><strong>Abstract.</strong> Visual sampling techniques represent a valuable resource for a rapid, non-invasive data acquisition for underwater monitoring purposes. Long-term monitoring projects usually requires the collection of large quantities of data, and the visual analysis of a human expert operator remains, in this context, a very time consuming task. It has been estimated that only the 1-2% of the acquired images are later analyzed by scientists (Beijbom et al., 2012). Strategies for the automatic recognition of benthic communities are required to effectively exploit all the information contained in visual data. Supervised learning methods, the most promising classification techniques in this field, are commonly affected by two recurring issues: the wide diversity of marine organism, and the small amount of labeled data.</p><p> In this work, we discuss the advantages offered by the use of annotated high resolution ortho-mosaics of seabed to classify and segment the investigated specimens, and we suggest several strategies to obtain a considerable per-pixel classification performance although the use of a reduced training dataset composed by a single ortho-mosaic. The proposed methodology can be applied to a large number of different species, making the procedure of marine organism identification an highly adaptable task.</p>

scholarly journals Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning

2019 ◽  
Author(s):  
Bingbing Xu ◽  
Huawei Shen ◽  
Qi Cao ◽  
Keting Cen ◽  
Xueqi Cheng
Keyword(s):  
Heat Kernel ◽  
Supervised Learning ◽  
Low Frequency ◽  
Weighted Average ◽  
Relevant Information ◽  
Semisupervised Learning ◽  
Heat Diffusion ◽  
Graph Structure ◽  
Convolutional Networks ◽  
Benchmark Datasets

Graph convolutional networks gain remarkable success in semi-supervised learning on graph-structured data. The key to graph-based semisupervised learning is capturing the smoothness of labels or features over nodes exerted by graph structure. Previous methods, spectral methods and spatial methods, devote to defining graph convolution as a weighted average over neighboring nodes, and then learn graph convolution kernels to leverage the smoothness to improve the performance of graph-based semi-supervised learning. One open challenge is how to determine appropriate neighborhood that reflects relevant information of smoothness manifested in graph structure. In this paper, we propose GraphHeat, leveraging heat kernel to enhance low-frequency filters and enforce smoothness in the signal variation on the graph. GraphHeat leverages the local structure of target node under heat diffusion to determine its neighboring nodes flexibly, without the constraint of order suffered by previous methods. GraphHeat achieves state-of-the-art results in the task of graph-based semi-supervised classification across three benchmark datasets: Cora, Citeseer and Pubmed.

scholarly journals The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Gulshan Kumar ◽  
Krishan Kumar
Keyword(s):  
Artificial Intelligence ◽  
Intrusion Detection ◽  
Supervised Learning ◽  
Ensemble Learning ◽  
Classification Performance ◽  
Intrusion Detection Systems ◽  
Future Directions ◽  
Comprehensive Review ◽  
Detection Systems ◽  
Combination Methods

In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI-) based techniques play prominent role in development of ensemble for intrusion detection (ID) and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular) during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1) architecture & approach followed; (2) different methods utilized in different phases of ensemble learning; (3) other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs).

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