scholarly journals Aggregate Linear Discriminate Analyzed Feature Extraction and Ensemble of Bootstrap with Knn Classifier for Malicious Tumour Detection

2019 ◽  
Vol 8 (3) ◽  
pp. 3686-3694
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Ensemble Classifier ◽  
Detection Accuracy ◽  
Ensemble Classifiers ◽  
Data Set ◽  
Tumour Detection ◽  
Tumour Classification ◽  
Classification Feature

Tumour detection medical applications utilize classification techniques to categorize malicious and nonmalicious tumour features to provide an efficient medical diagnosis of the human individual under investigation. One way to enable efficient classification, Feature extraction methods are used to eliminate the redundant features and obtain the most relevant features. However, the challenges concerning the dimension and quantum of tumour dataset persist. Toward this goal, this paper aims to maximize the malicious tumour classification accuracy using two reliable ensemble classifiers namely Bootstrap Aggregation and k-nearest neighbour. Tumour features extracted by Aggregate Linear Discriminate Analysis (LDA) and the feature distance is calculated with iterative scattering matrix algorithm. The extracted features are further refined by aggregation to select most effective feature values. After this, an ensemble classifier technique is employed to construct malicious and non-malicious tumour classes. The tumour classification based on an ensemble of bagging and knearest neighbour. Simulation is carried out on Tumour Repository data set to show that proposed ensemble classifiers have considerably better tumour detection accuracy than existing conventional techniques. Numerical performance evaluations show that 8% improvement by proposed method in tumour classification accuracy for malicious tumour detection in human individuals.

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

scholarly journals The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4749
Author(s):  
Shaorong Zhang ◽  
Zhibin Zhu ◽  
Benxin Zhang ◽  
Bao Feng ◽  
Tianyou Yu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Extraction Methods ◽  
Extraction Time ◽  
Classification Model ◽  
Discrete Wavelet ◽  
New Feature

The common spatial pattern (CSP) is a very effective feature extraction method in motor imagery based brain computer interface (BCI), but its performance depends on the selection of the optimal frequency band. Although a lot of research works have been proposed to improve CSP, most of these works have the problems of large computation costs and long feature extraction time. To this end, three new feature extraction methods based on CSP and a new feature selection method based on non-convex log regularization are proposed in this paper. Firstly, EEG signals are spatially filtered by CSP, and then three new feature extraction methods are proposed. We called them CSP-wavelet, CSP-WPD and CSP-FB, respectively. For CSP-Wavelet and CSP-WPD, the discrete wavelet transform (DWT) or wavelet packet decomposition (WPD) is used to decompose the spatially filtered signals, and then the energy and standard deviation of the wavelet coefficients are extracted as features. For CSP-FB, the spatially filtered signals are filtered into multiple bands by a filter bank (FB), and then the logarithm of variances of each band are extracted as features. Secondly, a sparse optimization method regularized with a non-convex log function is proposed for the feature selection, which we called LOG, and an optimization algorithm for LOG is given. Finally, ensemble learning is used for secondary feature selection and classification model construction. Combing feature extraction and feature selection methods, a total of three new EEG decoding methods are obtained, namely CSP-Wavelet+LOG, CSP-WPD+LOG, and CSP-FB+LOG. Four public motor imagery datasets are used to verify the performance of the proposed methods. Compared to existing methods, the proposed methods achieved the highest average classification accuracy of 88.86, 83.40, 81.53, and 80.83 in datasets 1–4, respectively. The feature extraction time of CSP-FB is the shortest. The experimental results show that the proposed methods can effectively improve the classification accuracy and reduce the feature extraction time. With comprehensive consideration of classification accuracy and feature extraction time, CSP-FB+LOG has the best performance and can be used for the real-time BCI system.

scholarly journals Improving the classification accuracy for IR spectroscopic diagnosis of stomach and colon malignancy using non-linear spectral feature extraction methods

The Analyst ◽  
2013 ◽  
Vol 138 (14) ◽  
pp. 4076 ◽  
Author(s):  
Sanguk Lee ◽  
Kyoungok Kim ◽  
Hyeseon Lee ◽  
Chi-Hyuck Jun ◽  
Hoeil Chung ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Spectral Feature ◽  
Extraction Methods ◽  
Non Linear ◽  
Ir Spectroscopic

scholarly journals Automatic catalog of RR Lyrae from ∼14 million VVV light curves: How far can we go with traditional machine-learning?

2020 ◽  
Vol 642 ◽  
pp. A58
Author(s):  
J. B. Cabral ◽  
F. Ramos ◽  
S. Gurovich ◽  
P. M. Granitto
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Broad Band ◽  
Ensemble Classifier ◽  
Light Curves ◽  
Ensemble Classifiers ◽  
Data Set ◽  
Rr Lyrae ◽  
Selection Step ◽  
Sampling Procedures

Context. The creation of a 3D map of the bulge using RR Lyrae (RRL) is one of the main goals of the VISTA Variables in the Via Lactea Survey (VVV) and VVV(X) surveys. The overwhelming number of sources undergoing analysis undoubtedly requires the use of automatic procedures. In this context, previous studies have introduced the use of machine learning (ML) methods for the task of variable star classification. Aims. Our goal is to develop and test an entirely automatic ML-based procedure for the identification of RRLs in the VVV Survey. This automatic procedure is meant to be used to generate reliable catalogs integrated over several tiles in the survey. Methods. Following the reconstruction of light curves, we extracted a set of period- and intensity-based features, which were already defined in previous works. Also, for the first time, we put a new subset of useful color features to use. We discuss in considerable detail all the appropriate steps needed to define our fully automatic pipeline, namely: the selection of quality measurements; sampling procedures; classifier setup, and model selection. Results. As a result, we were able to construct an ensemble classifier with an average recall of 0.48 and average precision of 0.86 over 15 tiles. We also made all our processed datasets available and we published a catalog of candidate RRLs. Conclusions. Perhaps most interestingly, from a classification perspective based on photometric broad-band data, our results indicate that color is an informative feature type of the RRL objective class that should always be considered in automatic classification methods via ML. We also argue that recall and precision in both tables and curves are high-quality metrics with regard to this highly imbalanced problem. Furthermore, we show for our VVV data-set that to have good estimates, it is important to use the original distribution more abundantly than reduced samples with an artificial balance. Finally, we show that the use of ensemble classifiers helps resolve the crucial model selection step and that most errors in the identification of RRLs are related to low-quality observations of some sources or to the increased difficulty in resolving the RRL-C type given the data.

scholarly journals Creating Ensemble Classifiers with Information Entropy Diversity Measure

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jiangbo Zou ◽  
Xiaokang Fu ◽  
Lingling Guo ◽  
Chunhua Ju ◽  
Jingjing Chen
Keyword(s):  
Information Entropy ◽  
Classification Accuracy ◽  
Ensemble Classifier ◽  
The Other ◽  
Ensemble Classification ◽  
Ensemble Classifiers ◽  
System Cost ◽  
Iterative Optimization ◽  
Diversity Measure ◽  
Maximum Accuracy

Ensemble classifiers improve the classification accuracy by incorporating the decisions made by its component classifiers. Basically, there are two steps to create an ensemble classifier: one is to generate base classifiers and the other is to align the base classifiers to achieve maximum accuracy integrally. One of the major problems in creating ensemble classifiers is the classification accuracy and diversity of the component classifiers. In this paper, we propose an ensemble classifier generating algorithm to improve the accuracy of an ensemble classification and to maximize the diversity of its component classifiers. In this algorithm, information entropy is introduced to measure the diversity of component classifiers, and a cyclic iterative optimization selection tactic is applied to select component classifiers from base classifiers, in which the number of component classifiers is dynamically adjusted to minimize system cost. It is demonstrated that our method has an obvious lower memory cost with higher classification accuracy compared with existing classifier methods.

Learning Advanced Brain Computer Interface Technology

2019 ◽  
Vol 15 (3) ◽  
pp. 14-27
Author(s):  
Wang Tao ◽  
Wu Linyan ◽  
Li Yanping ◽  
Gao Nuo ◽  
Zhang Weiran
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Support Vector ◽  
Wavelet Packet Analysis ◽  
Accuracy Rate ◽  
Desirable Outcome ◽  
Eeg Data ◽  
System Data

Feature extraction is an important step in electroencephalogram (EEG) processing of motor imagery, and the feature extraction of EEG directly affects the final classification results. Through the analysis of various feature extraction methods, this article finally selects Common Spatial Patterns (CSP) and wavelet packet analysis (WPA) to extract the feature and uses Support Vector Machine (SVM) to classify and compare these extracted features. For the EEG data provided by GRAZ University, the accuracy rate of feature extraction using CSP algorithm is 85.5%, and the accuracy rate of feature extraction using wavelet packet analysis is 92%. Then this paper analyzes the EEG data collected by Emotiv epoc+ system. The classification accuracy of wavelet packet extracted features can still be maintained at more than 80%, while the classification accuracy of CSP extracted feature is decreased obviously. Experimental results show that the method of wavelet packet analysis towards competition data and Emotiv epoc+ system data can both get a desirable outcome.

A SEIZURE DETECTION METHOD BASED ON WELL-SOLVED NONLINEAR AND NON-STATIONARY PROBLEMS WITH ELECTROENCEPHALOGRAPHIC SIGNALS

2018 ◽  
Vol 30 (05) ◽  
pp. 1850037
Author(s):  
Xia Zhang ◽  
Haijun Chen
Keyword(s):  
Feature Extraction ◽  
Epileptic Seizure ◽  
Classification Accuracy ◽  
Seizure Detection ◽  
Extraction Methods ◽  
Epileptic Seizure Detection ◽  
Healthy State ◽  
Cepstral Coefficients ◽  
The Moment ◽  
Stationary Problems

The main focus of this paper is to solve the nonlinear and non-stationary problems in electroencephalographic (EEG) signals, which has been solved by the proposed method by using convolutional neural networks (CNN) as the classifiers and assembling Local Mean Decomposition (LMD) and cepstral coefficients as the feature extraction methods to achieve epileptic seizure detection with signal analysis and processing. In this proposed method, LMD and cepstral coefficients have been employed to solve the nonlinear and non-stationary problems in feature extraction and infusion, and then, the feature can be employed to feed to the recognition engine named CNN, and finally, the epileptic seizure detection can be achieved by this step. Publicly available EEG database from the University of Bonn (UoB), Germany had been used to verify the effectiveness and robustness of this proposed method on feature extraction. The complete dataset of total 7960 EEG segments, three recognition problems marked as AB versus CD versus E, the average classification accuracy of these segments can be generally obtained as highly as 99.84%, the maximal classification accuracy is 99.87%, and the lowest recognition accuracy is 98.74%. To the best of our knowledge, the excellent performance of the proposed method has shown that this method can be employed to track the patient’s healthy state and monitor the moment of epilepsy seizure.

scholarly journals Klasifikasi Lukisan Karya Van Gogh Menggunakan Convolutional Neural Network-Support Vector Machine

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yohannes Yohannes ◽  
Daniel Udjulawa ◽  
Febbiola Febbiola
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Support Vector ◽  
Data Set ◽  
Van Gogh ◽  
Network Support ◽  
A Value ◽  
Best Value ◽  
Random Optimization ◽  
Grid Optimization

Painting is a work of art with various strokes, textures, and color gradations so that a painting that is synonymous with beauty is created. The various paintings created have characteristics, such as the paintings by Van Gogh, which have tightly arranged strokes, creating a repetitive and patterned impression. This study classifies paintings by Van Gogh or not by using the VGG-19 and ResNet-50 feature extraction methods. The SVM method is used as a classification method with two optimizations, namely random and grid optimization in the linear kernel. The data set used consisted of 124 Van Gogh paintings and 207 paintings by other painters. The use of VGG-19 feature extraction using grid optimization has the best value of 93,28% using the use of random optimization which has a value of 92,89%. The use of ResNet-50 using grid optimization with the best value of 90,28% using the use of random optimization which has a value of 90,15%. The extraction feature of VGG-19 is better than ResNet-50 in paintings by Van Gogh or not.

scholarly journals An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4495 ◽  
Author(s):  
Theekshana Dissanayake ◽  
Yasitha Rajapaksha ◽  
Roshan Ragel ◽  
Isuru Nawinne
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Emotion Recognition ◽  
Ensemble Learning ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Learning Approach ◽  
Human Emotion ◽  
Different Types ◽  
Human Emotions

Recently, researchers in the area of biosensor based human emotion recognition have used different types of machine learning models for recognizing human emotions. However, most of them still lack the ability to recognize human emotions with higher classification accuracy incorporating a limited number of bio-sensors. In the domain of machine learning, ensemble learning methods have been successfully applied to solve different types of real-world machine learning problems which require improved classification accuracies. Emphasising on that, this research suggests an ensemble learning approach for developing a machine learning model that can recognize four major human emotions namely: anger; sadness; joy; and pleasure incorporating electrocardiogram (ECG) signals. As feature extraction methods, this analysis combines four ECG signal based techniques, namely: heart rate variability; empirical mode decomposition; with-in beat analysis; and frequency spectrum analysis. The first three feature extraction methods are well-known ECG based feature extraction techniques mentioned in the literature, and the fourth technique is a novel method proposed in this study. The machine learning procedure of this investigation evaluates the performance of a set of well-known ensemble learners for emotion classification and further improves the classification results using feature selection as a prior step to ensemble model training. Compared to the best performing single biosensor based model in the literature, the developed ensemble learner has the accuracy gain of 10.77%. Furthermore, the developed model outperforms most of the multiple biosensor based emotion recognition models with a significantly higher classification accuracy gain.

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