A Kind of Combination Feature Division and Diversity Measure of Multi-Classifier Selective Ensemble Algorithm

2011 ◽  
Vol 63-64 ◽  
pp. 55-58
Author(s):  
Yan Wang ◽  
Xiu Xia Wang ◽  
Sheng Lai
Keyword(s):  
Classification Accuracy ◽  
Extraction Method ◽  
Principal Component ◽  
Feature Extraction Method ◽  
Diversity Measure ◽  
High Classification Accuracy ◽  
Selective Ensemble ◽  
Ensemble Algorithm ◽  
Bagging Method ◽  
Classifier Diversity

In ensemble learning, in order to improve the performance of individual classifiers and the diversity of classifiers, from the classifiers generation and combination, this paper proposes a kind of combination feature division and diversity measure of multi-classifier selective ensemble algorithm. The algorithm firstly applied bagging method to create some feature subsets, Secondly using principal component analysis of feature extraction method on each feature subsets, then select classifiers with high-classification accuracy; finally before classifier combination we use classifier diversity measure method select diversity classifiers. Experimental results prove that classification accuracy of the algorithm is obviously higher than popular bagging algorithm.

PCA PLUS F-LDA: A NEW APPROACH TO FACE RECOGNITION

2007 ◽  
Vol 21 (06) ◽  
pp. 1059-1068 ◽  
Author(s):  
HUIYUAN WANG ◽  
ZENGFENG WANG ◽  
YAN LENG ◽  
XIAOJUAN WU ◽  
QING LI
Keyword(s):  
Face Recognition ◽  
Classification Accuracy ◽  
Extraction Method ◽  
Principal Component ◽  
Computation Complexity ◽  
Scatter Matrix ◽  
New Approach ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
New Feature

A new feature extraction method for face recognition based on principal component analysis (PCA) and fractional-step linear discriminant analysis (F-LDA) is given in this paper. In order to reduce the computation complexity, PCA is first used to reduce the dimension. In addition, before using F-LDA, we transform the pooled within-class scatter matrix into an identity matrix. The proposed method is tested on AR and UMIST face databases. Experiment results show that our method gains higher classification accuracy than other existing methods used in the experiment.

A new combined transient extraction method coupled with WO3 gas sensors for polluting gases classification

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rabeb Faleh ◽  
Sami Gomri ◽  
Khalifa Aguir ◽  
Abdennaceur Kachouri
Keyword(s):  
Feature Extraction ◽  
Gas Sensors ◽  
Classification Accuracy ◽  
Extraction Method ◽  
Support Vector ◽  
Classification Rate ◽  
Content Type ◽  
Feature Extraction Method ◽  
Combined Features ◽  
Discrimination Capacity

Purpose The purpose of this paper is to deal with the classification improvement of pollutant using WO3 gases sensors. To evaluate the discrimination capacity, some experiments were achieved using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol via four WO3 sensors. Design/methodology/approach To improve the classification accuracy and enhance selectivity, some combined features that were configured through the principal component analysis were used. First, evaluate the discrimination capacity; some experiments were performed using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol, via four WO3 sensors. To this end, three features that are derivate, integral and the time corresponding to the peak derivate have been extracted from each transient sensor response according to four WO3 gas sensors used. Then these extracted parameters were used in a combined array. Findings The results show that the proposed feature extraction method could extract robust information. The Extreme Learning Machine (ELM) was used to identify the studied gases. In addition, ELM was compared with the Support Vector Machine (SVM). The experimental results prove the superiority of the combined features method in our E-nose application, as this method achieves the highest classification rate of 90% using the ELM and 93.03% using the SVM based on Radial Basis Kernel Function SVM-RBF. Originality/value Combined features have been configured from transient response to improve the classification accuracy. The achieved results show that the proposed feature extraction method could extract robust information. The ELM and SVM were used to identify the studied gases.

scholarly journals Phishing Scam Detection on Ethereum: Towards Financial Security for Blockchain Ecosystem

2020 ◽  
Author(s):  
Weili Chen ◽  
Xiongfeng Guo ◽  
Zhiguang Chen ◽  
Zibin Zheng ◽  
Yutong Lu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Systematic Approach ◽  
Financial Security ◽  
Feature Extraction Method ◽  
Blockchain Technology ◽  
Ensemble Algorithm ◽  
Identification Model

In recent years, blockchain technology has created a new cryptocurrency world and has attracted a lot of attention. It also is rampant with various scams. For example, phishing scams have grabbed a lot of money and has become an important threat to users' financial security in the blockchain ecosystem. To help deal with this issue, this paper proposes a systematic approach to detect phishing accounts based on blockchain transactions and take Ethereum as an example to verify its effectiveness. Specifically, we propose a graph-based cascade feature extraction method based on transaction records and a lightGBM-based Dual-sampling Ensemble algorithm to build the identification model. Extensive experiments show that the proposed algorithm can effectively identify phishing scams.

Research of Face Recognition Methods Based on Binding Feature Extraction

2014 ◽  
Vol 568-570 ◽  
pp. 668-671
Author(s):  
Yi Long ◽  
Fu Rong Liu ◽  
Guo Qing Qiu
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Principal Component ◽  
Face Image ◽  
Feature Extraction Method ◽  
Histogram Feature

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.

scholarly journals Video-Based Sentiment Analysis with hvnLBP-TOP Feature and bi-LSTM

2019 ◽  
Vol 33 ◽  
pp. 9963-9964 ◽  
Author(s):  
Haoran Li ◽  
Hua Xu
Keyword(s):  
Sentiment Analysis ◽  
Extraction Method ◽  
Short Term Memory ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Short Term ◽  
Term Memory ◽  
Feature Extraction Method ◽  
Long Short Term Memory ◽  
New Feature

In this paper, we propose a new feature extraction method called hvnLBP-TOP for video-based sentiment analysis. Furthermore, we use principal component analysis (PCA) and bidirectional long short term memory (bi-LSTM) for dimensionality reduction and classification. We achieved an average recognition accuracy of 71.1% on the MOUD dataset and 63.9% on the CMU-MOSI dataset.

Single-trial detection of EEG error-related potentials in serial visual presentation paradigm

2021 ◽  
Author(s):  
Praveen K. Parashiva ◽  
Vinod A Prasad
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Extraction Method ◽  
Image Annotation ◽  
Visual Presentation ◽  
Cognitive State ◽  
Single Trial ◽  
Feature Extraction Method ◽  
Related Potentials ◽  
Trial Basis

Abstract When the outcome of an event is not the same as expected, the cognitive state that monitors performance elicits a time-locked brain response termed as Error-Related Potential (ErrP). Objective – In the existing work, ErrP is not recorded when there is a disassociation between an object and its description. The objective of this work is to propose a Serial Visual Presentation (SVP) experimental paradigm to record ErrP when an image and its label are disassociated. Additionally, this work aims to propose a novel method for detecting ErrP on a single-trial basis. Method – The method followed in this work includes designing of SVP paradigm in which labeled images from six categories (bike, car, flower, fruit, cat, and dog) are presented serially. In this work, a text (visual) or an audio clip describing the image in one word is presented as the label. Further, the ErrP is detected on a single-trial basis using novel electrode-averaged features. Results - The ErrP data recorded from 11 subjects’ have consistent characteristics compared to existing ErrP literature. Detection of ErrP on a single-trial basis is carried out using a novel feature extraction method on two type labeling types separately. The best average classification accuracy achieved is 69.09±4.70% and 63.33±4.56% for the audio and visual type of labeling the image, respectively. The proposed feature extraction method achieved higher classification accuracy when compared with two existing feature extraction methods. Significance - The significance of this work is that it can be used as a Brain-Computer Interface (BCI) system for quantitative evaluation and treatment of mild cognitive impairment. This work can also find non-clinical BCI applications such as image annotation.

scholarly journals Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer’s disease and mild cognitive impairment

2019 ◽  
Vol 12 ◽  
pp. 175628641983868 ◽  
Author(s):  
Yupeng Li ◽  
Jiehui Jiang ◽  
Jiaying Lu ◽  
Juanjuan Jiang ◽  
Huiwei Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Cognitive Impairment ◽  
Fdg Pet ◽  
Classification Accuracy ◽  
Extraction Method ◽  
Feature Extraction Method ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Alzheimer’s disease (AD) is the most common form of progressive and irreversible dementia, and accurate diagnosis of AD at its prodromal stage is clinically important. Currently, computer-aided diagnosis of AD and mild cognitive impairment (MCI) using 18F-fluorodeoxy-glucose positron emission tomography (18F-FDG PET) imaging is usually based on low-level imaging features or deep learning methods, which have difficulties in achieving sufficient classification accuracy or lack clinical significance. This research therefore aimed to implement a new feature extraction method known as radiomics, to improve the classification accuracy and discover high-order features that can reveal pathological information. Methods: In this study, 18F-FDG PET and clinical assessments were collected in a cohort of 422 individuals [including 130 with AD, 130 with MCI, and 162 healthy controls (HCs)] from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 44 individuals (including 22 with AD, and 22 HCs) from Huashan Hospital, Shanghai, China. First, we performed a group comparison using a two-sample Student’s t test to determine the regions of interest (ROIs) based on 30 AD patients and 30 HCs from ADNI cohorts. Second, based on two time scans of 32 HCs from ADNI cohorts, we used Cronbach’s alpha coefficient for radiomic feature stability analyses. Pearson’s correlation coefficients were regarded as a feature selection criterion, to select effective features associated with the clinical cognitive scale [clinical dementia rating scale in its sum of boxes (CDRSB); Alzheimer’s disease assessment scale (ADAS)] with 500-times cross-validation. Finally, a support vector machine (SVM) was used to test the ability of the radiomic features to classify HCs, MCI and AD patients. Results: As a result, we identified brain regions which were mainly distributed in the temporal, occipital and frontal areas as ROIs. A total of 168 radiomic features of AD were stable (alpha > 0.8). The classification experiment led to maximal accuracies of 91.5%, 83.1% and 85.9% for classifying AD versus HC, MCI versus HCs and AD versus MCI. Conclusion: The research in this paper proved that the novel approach based on high-order radiomic features extracted from 18F-FDG PET brain images that can be used for AD and MCI computer-aided diagnosis.

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