scholarly journals Classification of Marine Vessels with Multi-Feature Structure Fusion

2019 ◽  
Vol 9 (10) ◽  
pp. 2153 ◽  
Author(s):  
Erhu Zhang ◽  
Kelu Wang ◽  
Guangfeng Lin
Keyword(s):  
Promising Result ◽  
Infrared Image ◽  
Feature Space ◽  
Classification Performance ◽  
Feature Structure ◽  
Redundant Feature ◽  
Feature Dimension ◽  
Spectral Regression ◽  
Marine Vessels

The classification of marine vessels is one of the important problems of maritime traffic. To fully exploit the complementarity between different features and to more effectively identify marine vessels, a novel feature structure fusion method based on spectral regression discriminant analysis (SF-SRDA) was proposed. Firstly, we selected the different convolutional neural network features that better describe the characteristics of ships, and constructed the features based on graphs by the similarity metric. Then we weighed the concatenate multi-feature and fused their structures according to the linear relationship assumption. Finally, we constructed the optimization formula to solve the fusion features and structure by using spectral regression discriminant analyses. Experiments on the VAIS dataset show that the proposed SF-SRDA method can reduce the feature dimension from the original 102,400 dimensions to 5 dimensions, that the classification accuracy of visible images can reach 87.60%, and that that of the infrared image can reach 74.68% at daytime. The experimental results demonstrate that the proposed method can not only extract the optimal features from the original redundant feature space, but also greatly reduce the dimensions of the feature. Furthermore, the classification performance of SF-SRDA also gets a promising result.

scholarly journals Improving the classification of neuropsychiatric conditions using gene ontology terms as features

2018 ◽  
Author(s):  
Thomas P. Quinn ◽  
Samuel C. Lee ◽  
Svetha Venkatesh ◽  
Thin Nguyen
Keyword(s):  
Gene Ontology ◽  
Feature Space ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Biological Importance ◽  
Process Database ◽  
High Level ◽  
Neuropsychiatric Conditions

AbstractAlthough neuropsychiatric disorders have a well-established genetic background, their specific molecular foundations remain elusive. This has prompted many investigators to design studies that identify explanatory biomarkers, and then use these biomarkers to predict clinical outcomes. One approach involves using machine learning algorithms to classify patients based on blood mRNA expression from high-throughput transcriptomic assays. However, these endeavours typically fail to achieve the high level of performance, stability, and generalizability required for clinical translation. Moreover, these classifiers can lack interpretability because informative genes do not necessarily have relevance to researchers. For this study, we hypothesized that annotation-based classifiers can improve classification performance, stability, generalizability, and interpretability. To this end, we evaluated the performance of four classification algorithms on six neuropsychiatric data sets using four annotation databases. Our results suggest that the Gene Ontology Biological Process database can transform gene expression into an annotation-based feature space that improves the performance and stability of blood-based classifiers for neuropsychiatric conditions. We also show how annotation features can improve the interpretability of classifiers: since annotation databases are often used to assign biological importance to genes, annotation-based classifiers are easy to interpret because the biological importance of the features are the features themselves. We found that using annotations as features improves the performance and stability of classifiers. We also noted that the top ranked annotations tend contain the top ranked genes, suggesting that the most predictive annotations are a superset of the most predictive genes. Based on this, and the fact that annotations are used routinely to assign biological importance to genetic data, we recommend transforming gene-level expression into annotation-level expression prior to the classification of neuropsychiatric conditions.

scholarly journals Fingerprint Intramodal Biometric System Based on ABC Feature Fusion

2021 ◽  
pp. 1-10
Author(s):  
J. O. Jooda ◽  
A. O. Oke ◽  
E. O. Omidiora ◽  
O. T. Adedeji
Keyword(s):  
Feature Selection ◽  
Feature Fusion ◽  
Feature Space ◽  
Classification Performance ◽  
High Dimensionality ◽  
Biometric System ◽  
Bee Colony ◽  
Optimizing Algorithm ◽  
Level Fusion

Unimodal biometrics system (UBS) drawbacks include noisy data, intra-class variance, inter-class similarities, non-universality, which all affect the system's classification performance. Intramodal fingerprint fusion can overcome the limitations imposed by UBS when features are fused at the feature level as it is a good approach to boost the performance of the biometric system. However, feature level fusion leads to high dimensionality of feature space which can be overcame by Feature Selection (FS). FS improves the performance of classification by selecting only relevant and useful information from extracted feature sets being an optimization problem. Artificial Bee Colony (ABC) is an optimizing algorithm that has been frequently used in solving FS problems because of its simple concept, use of few control parameters, easy implementation and good exploration characteristics. ABC was proposed for optimized feature selection prior to the classification of Fingerprint Intramodal Biometric System (FIBS). Performance evaluation of ABC-based FIBS showed the system had a Sensitivity of 97.69% and RA of 96.76%. The developed ABC optimized feature selection reduced the high dimensionality of features space prior to classification tasks thereby increasing sensitivity and recognition accuracy of FIBS.

scholarly journals Human Stress and StO2: Database, Features, and Classification of Emotional and Physical Stress

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 962
Author(s):  
Xinyu Liu ◽  
Yuhao Shan ◽  
Min Peng ◽  
Huanyu Chen ◽  
Tong Chen
Keyword(s):  
Classification Performance ◽  
Physical Stress ◽  
Blood Oxygen Saturation ◽  
Classification Rate ◽  
Physiological Signal ◽  
Stress Classification ◽  
Human Stress ◽  
Stress States ◽  
Feature Dimension

Emotional and physical stress can cause various health problems. In this paper, we used tissue blood oxygen saturation (StO2), a newly proposed physiological signal, to classify the human stress. We firstly constructed a public StO2 database including 42 volunteers subjected to two types of stress. During the physical stress experiment, we observed that the facial StO2 right after the stress can be either increased or decreased comparing to the baseline. We investigated the StO2 feature combinations for the classification and found that the average StO2 values from left cheek, chin, and the middle of the eyebrow can provide the highest classification rate of 95.56%. Comparison with other stress classification method shows that StO2 based method can provide best classification performance with lowest feature dimension. These results suggest that facial StO2 can be used as a promising features to identify stress states, including emotional and physical stress.

scholarly journals Genetic Algorithm Based Feature Selection In a Recognition Scheme Using Adaptive Neuro Fuzzy Techniques

2010 ◽  
Vol 5 (4) ◽  
pp. 458 ◽  
Author(s):  
Mahua Bhattacharya ◽  
Arpita Das
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Feature Space ◽  
Classification Performance ◽  
Significant Feature ◽  
Feature Subset ◽  
Neuro Fuzzy ◽  
Feature Spaces ◽  
Fuzzy Techniques

The problem of feature selection consists of finding a significant feature subset of input training as well as test patterns that enable to describe all information required to classify a particular pattern. In present paper we focus in this particular problem which plays a key role in machine learning problems. In fact, before building a model for feature selection, our goal is to identify and to reject the features that degrade the classification performance of a classifier. This is especially true when the available input feature space is very large, and need exists to develop an efficient searching algorithm to combine these features spaces to a few significant one which are capable to represent that particular class. Presently, authors have described two approaches for combining the large feature spaces to efficient numbers using Genetic Algorithm and Fuzzy Clustering techniques. Finally the classification of patterns has been achieved using adaptive neuro-fuzzy techniques. The aim of entire work is to implement the recognition scheme for classification of tumor lesions appearing in human brain as space occupying lesions identified by CT and MR images. A part of the work has been presented in this paper. The proposed model indicates a promising direction for adaptation in a changing environment.

scholarly journals Classification of Error-Diffused Halftone Images Based on Spectral Regression Kernel Discriminant Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Zhigao Zeng ◽  
Zhiqiang Wen ◽  
Shengqiu Yi ◽  
Sanyou Zeng ◽  
Yanhui Zhu ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Accuracy Rate ◽  
Kernel Discriminant Analysis ◽  
Image Patches ◽  
Classification Of Error ◽  
Added Benefit ◽  
Feature Dimension ◽  
Spectral Regression ◽  
Halftone Images

This paper proposes a novel algorithm to solve the challenging problem of classifying error-diffused halftone images. We firstly design the class feature matrices, after extracting the image patches according to their statistics characteristics, to classify the error-diffused halftone images. Then, the spectral regression kernel discriminant analysis is used for feature dimension reduction. The error-diffused halftone images are finally classified using an idea similar to the nearest centroids classifier. As demonstrated by the experimental results, our method is fast and can achieve a high classification accuracy rate with an added benefit of robustness in tackling noise.

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

scholarly journals Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Kun Zeng ◽  
Yibin Xu ◽  
Ge Lin ◽  
Likeng Liang ◽  
Tianyong Hao
Keyword(s):  
Clinical Trial ◽  
Ensemble Learning ◽  
Metric Learning ◽  
Classification Performance ◽  
Ensemble Model ◽  
Automated Classification ◽  
Eligibility Criteria ◽  
Data Imbalance ◽  
The Impact

Abstract Background Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data. Methods An ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories. Results Our ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement. Conclusions A model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance.

scholarly journals Space Precession Target Classification Based on Radar High-Resolution Range Profiles

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yizhe Wang ◽  
Cunqian Feng ◽  
Yongshun Zhang ◽  
Sisan He
Keyword(s):  
Parameter Extraction ◽  
Classification Performance ◽  
Support Vector ◽  
Electromagnetic Data ◽  
Feature Extractor ◽  
Different Types ◽  
Radar Echo ◽  
High Level ◽  
Cone Target

Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.

scholarly journals Attention-based deep learning networks for identification of human gait using radar micro-Doppler spectrograms

2021 ◽  
pp. 1-6
Author(s):  
Hannah Garcia Doherty ◽  
Roberto Arnaiz Burgueño ◽  
Roeland P. Trommel ◽  
Vasileios Papanastasiou ◽  
Ronny I. A. Harmanny
Keyword(s):  
Neural Networks ◽  
Feature Vector ◽  
Classification Performance ◽  
Input Image ◽  
Human Gait ◽  
Learning Networks ◽  
Class Label ◽  
Deep Convolutional Neural Networks ◽  
Network Layers ◽  
Feature Dimension

Abstract Identification of human individuals within a group of 39 persons using micro-Doppler (μ-D) features has been investigated. Deep convolutional neural networks with two different training procedures have been used to perform classification. Visualization of the inner network layers revealed the sections of the input image most relevant when determining the class label of the target. A convolutional block attention module is added to provide a weighted feature vector in the channel and feature dimension, highlighting the relevant μ-D feature-filled areas in the image and improving classification performance.

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