scholarly journals A new model for iris data set classification based on linear support vector machine parameter's optimization

2020 ◽  
Vol 10 (1) ◽  
pp. 1079
Author(s):  
Zahraa Faiz Hussain ◽  
Hind Raad Ibraheem ◽  
Mohammad Alsajri ◽  
Ahmed Hussein Ali ◽  
Mohd Arfian Ismail ◽  
...  
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Set ◽  
Principle Components Analysis ◽  
Web Text Mining ◽  
Linear Svm ◽  
Iris Data

Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.

Ensemble of data mining methods for gene ranking

2012 ◽  
Vol 60 (3) ◽  
pp. 461-470 ◽  
Author(s):  
A. Wiliński ◽  
S. Osowski
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Gaussian Kernel ◽  
Graphical Form ◽  
Support Vector ◽  
Svm Classifier ◽  
Gene Expressions ◽  
Data Set ◽  
Pixel Intensity ◽  
Mining Methods

Abstract The paper presents the ensemble of data mining methods for discovering the most important genes and gene sequences generated by the gene expression arrays, responsible for the recognition of a particular type of cancer. The analyzed methods include the correlation of the feature with a class, application of the statistical hypotheses, the Fisher measure of discrimination and application of the linear Support Vector Machine for characterization of the discrimination ability of the features. In the first step of ranking we apply each method individually, choosing the genes most often selected in the cross validation of the available data set. In the next step we combine the results of different selection methods together and once again choose the genes most frequently appearing in the selected sets. On the basis of this we form the final ranking of the genes. The most important genes form the input information delivered to the Support Vector Machine (SVM) classifier, responsible for the final recognition of tumor from non-tumor data. Different forms of checking the correctness of the proposed ranking procedure have been applied. The first one is relied on mapping the distribution of selected genes on the two-coordinate system formed by two most important principal components of the PCA transformation and applying the cluster quality measures. The other one depicts the results in the graphical form by presenting the gene expressions in the form of pixel intensity for the available data. The final confirmation of the quality of the proposed ranking method are the classification results of recognition of the cancer cases from the non-cancer (normal) ones, performed using the Gaussian kernel SVM. The results of selection of the most significant genes used by the SVM for recognition of the prostate cancer cases from normal cases have confirmed a good accuracy of results. The presented methodology is of potential use for practical application in bioinformatics.

Gaits Classification of Normal vs. Patients by Wireless Gait Sensor and Support Vector Machine (SVM) Classifier

2017 ◽  
Vol 5 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Taro Nakano ◽  
B.T. Nukala ◽  
J. Tsay ◽  
Steven Zupancic ◽  
Amanda Rodriguez ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Human Subjects ◽  
Wearable Sensors ◽  
Normal Subjects ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Dynamic ◽  
Linear Svm ◽  
Dynamic Gait

Due to the serious concerns of fall risks for patients with balance disorders, it is desirable to be able to objectively identify these patients in real-time dynamic gait testing using inexpensive wearable sensors. In this work, the authors took a total of 49 gait tests from 7 human subjects (3 normal subjects and 4 patients), where each person performed 7 Dynamic Gait Index (DGI) tests by wearing a wireless gait sensor on the T4 thoracic vertebra. The raw gait data is wirelessly transmitted to a near-by PC for real-time gait data collection. To objectively identify the patients from the gait data, the authors used 4 different types of Support Vector Machine (SVM) classifiers based on the 6 features extracted from the raw gait data: Linear SVM, Quadratic SVM, Cubic SVM, and Gaussian SVM. The Linear SVM, Quadratic SVM and Cubic SVM all achieved impressive 98% classification accuracy, with 95.2% sensitivity and 100% specificity in this work. However, the Gaussian SVM classifier only achieved 87.8% accuracy, 71.7% sensitivity, and 100% specificity. The results obtained with this small number of human subjects indicates that in the near future, the authors should be able to objectively identify balance-disorder patients from normal subjects during real-time dynamic gaits testing using intelligent SVM classifiers.

scholarly journals Building attack detection system base on machine learning

2021 ◽  
Vol 6 (2) ◽  
pp. 018-032
Author(s):  
Rasha Thamer Shawe ◽  
Kawther Thabt Saleh ◽  
Farah Neamah Abbas
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Support Vector ◽  
Data Set ◽  
Kdd Cup 99

These days, security threats detection, generally discussed to as intrusion, has befitted actual significant and serious problem in network, information and data security. Thus, an intrusion detection system (IDS) has befitted actual important element in computer or network security. Avoidance of such intrusions wholly bases on detection ability of Intrusion Detection System (IDS) which productions necessary job in network security such it identifies different kinds of attacks in network. Moreover, the data mining has been playing an important job in the different disciplines of technologies and sciences. For computer security, data mining are presented for serving intrusion detection System (IDS) to detect intruders accurately. One of the vital techniques of data mining is characteristic, so we suggest Intrusion Detection System utilizing data mining approach: SVM (Support Vector Machine). In suggest system, the classification will be through by employing SVM and realization concerning the suggested system efficiency will be accomplish by executing a number of experiments employing KDD Cup’99 dataset. SVM (Support Vector Machine) is one of the best distinguished classification techniques in the data mining region. KDD Cup’99 data set is utilized to execute several investigates in our suggested system. The experimental results illustration that we can decrease wide time is taken to construct SVM model by accomplishment suitable data set pre-processing. False Positive Rate (FPR) is decrease and Attack detection rate of SVM is increased .applied with classification algorithm gives the accuracy highest result. Implementation Environment Intrusion detection system is implemented using Mat lab 2015 programming language, and the examinations have been implemented in the environment of Windows-7 operating system mat lab R2015a, the processor: Core i7- Duo CPU 2670, 2.5 GHz, and (8GB) RAM.

scholarly journals Using Support Vector Machine Detection of Breast Cancer in Early stage

2020 ◽  
pp. 213-216
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Sensitivity Specificity

The Breast Cancer is disease which tremendously increased in women’s nowadays. Mammography is technique of low-powered X-ray diagnosis approach for detection and diagnosis of cancer diseases at early stage. The proposed system shows the solution of two problems. First shows to detect tumors as suspicious regions with a weak contrast to their background and second shows way to extract features which categorize tumors. Hence this classification can be done with SVM, a great method of statistical learning has made significant achievement in various field. Discovered in the early 90’s, which led to an interest in machine learning? Here the different types of tumor like Benign, Malignant, or Normal image are classified using the SVM classifier. This techniques shows how easily we can detect region of tumor is present in mammogram images with more than 80% of accuracy rates for linear classification using SVM. The 10-fold cross validation to get an accurate outcome is been used by proposed system. The Wisconsin breast cancer diagnosis data set is referred from UCI machine learning repository. The considering accuracy, sensitivity, specificity, false discovery rate, false omission rate and Matthews’s correlation coefficient is appraised in the proposed system. This Provides good result for both training and testing phase. The techniques also shows accuracy of 98.57% and 97.14% by use of Support Vector Machine and K-Nearest Neighbors

scholarly journals Forecasting Cases of Dengue Hemorrhagic Fever Using the Backpropagation, Gaussians and Support-Vector Machine Methods

2021 ◽  
Vol 5 (2) ◽  
pp. 335-341
Author(s):  
I Made Yudha Arya Dala ◽  
I Ketut Gede Darma Putra ◽  
Putu Wira Buana
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Aedes Aegypti ◽  
Dengue Virus ◽  
Dengue Fever ◽  
Error Rate ◽  
Support Vector ◽  
Preventive Action ◽  
Data Set ◽  
Predictive Values

Dengue disease has been known to the people of Indonesia since 1779. The Aedes mosquito has two types, namely Aedes aegypti and Aedes albopictus. Aedes aegypti is a mosquito that carries the dengue virus. The dengue fever cases in Bali province tend to increase from year to year, especially when approaching the rainy season. The government's preventive action is needed to tackle the spread of the dengue virus and casualties. Data mining attempts to extract known knowledge or use historical data to find regularity patterns and relationships in a set of data. In this study, data mining predicts the number of dengue cases in Bali's province. The prediction uses several database variables to predict future variables' values, which are not currently known. The process of estimating predictive values ​​based on patterns in a data set. This forecasting aims to assist the government in predicting dengue fever cases in the coming period to prepare appropriate prevention efforts. Forecasting dengue fever cases are carried out using three methods: backpropagation, gaussians, and support-vector machine. The amount of data used was 528 sample data, from 2008 to 2018. The results obtained are that the backpropagation method is better at predicting dengue fever cases with a MAPE error rate of 0.025. Simultaneously, the gaussian method has a MAPE error rate of 0.035, and support-vector machine has a MAPE error rate of 0.060.  

scholarly journals Weed Detection and Classification using ICA Based SVM Classifier

2020 ◽  
Vol 8 (5) ◽  
pp. 1557-1560
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Feature Weighting ◽  
Training Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Problems ◽  
Weed Detection ◽  
Data Set

Support vector machine (SVM) is a commonly known efficient supervised learning algorithm for classification problems. However, the classification accuracy of the SVM classifier depends on its training parameters and the training data set as well. The main objective of this paper is to optimize its parameters and feature weighting in order to improve the strength of the SVM simultaneously. In this paper, the Imperialist Competitive Algorithm based Support Vector Machine (ICA-SVM) classifier is proposed to classify the efficient weed detection. This enhanced ICA-SVM classifier is able to select the appropriate input features and to optimize the parameters of SVM and is improving the classification accuracy. Experimental results show that the ICA-SVM classification algorithm reduces the computational complexity tremendously and improves classification Accuracy.

Utilising Neural Network and Support Vector Machine for Gene Expression Classification

2007 ◽  
Vol 06 (04) ◽  
pp. 251-260 ◽  
Author(s):  
Keivan Kianmehr ◽  
Hongchao Zhang ◽  
Konstantin Nikolov ◽  
Tansel Özyer ◽  
Reda Alhajj
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Knowledge Management ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Management Technique ◽  
Data Set ◽  
Gene Expression Classification ◽  
Learning Techniques ◽  
Expression Classification

Bioinformatics is the science of managing, mining and interpreting information from biological sequences and structures. In this paper, we discuss two data-mining techniques that can be applied in bioinformatics: Neural Networks (NN) and Support Vector Machines (SVMs), and their application in gene expression classification. First, we provide a description of the two techniques. Then, we propose a new method that combines both SVM and NN. This way, we provide an effective knowledge management technique by utilising machine-learning techniques within the data-mining process. The knowledge obtained from the process is valuable as it is not possible to discover the same kind of knowledge using classical query processing or knowledge management techniques. Finally, we present the results obtained from our method and the results obtained from SVM alone on a sample data set.

Multichannel Vibration Fault Diagnosis for Rolling Bearings Based on QPCA and SVM

2011 ◽  
Vol 199-200 ◽  
pp. 927-930
Author(s):  
Zi Fa Li ◽  
Jin Guo Li
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Support Vector ◽  
Rolling Bearings ◽  
Data Set ◽  
Vibration Signals ◽  
Principle Components Analysis ◽  
Components Analysis ◽  
Pure Quaternion

A new method had been proposed in this paper of fault diagnosis for rolling bearings based on multichannel vibration signals and QPCA-SVM-based method. The vibration signals were obtained by some multi-sensors with three channels X, Y, Z, that were orthogonal axes. The three orthogonal axes signals were constructed a pure quaternion sequences as samples for processing. The pure quaternion sequences data set was processed by quaternion principle components analysis (QPCA) for feature extraction, and then combined with pattern recognition tools support vector machine (SVM) for classifying some faults patterns. The experimental results indicated its efficiency, and it provided a method for fault diagnosis on multichannel vibration signals.

scholarly journals Differential Evolution Tuned Support Vector Machine for Autistic Spectrum Disorder Diagnosis

2019 ◽  
Vol 8 (2) ◽  
pp. 3861-3870
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Optimization Algorithm ◽  
Autistic Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Classification Model ◽  
Support Vector ◽  
Data Set ◽  
Autistic Spectrum

Autistic Spectrum Disorder (ASD) is a brain developmental disorder which weakens the ability to communicate and interact with others. A child with autism spectrum disorder may have different, repetitive patterns of behaviour, interests or activities, including some specific signs. To diagnose the behaviour of ASD and identify the level of disease on the human is still a challenging task for the doctors. Only by the trained and experienced physician can identify the ASD immediately. The data set for autism problem consist of number of causes and the results based on the symptoms for ASD. So, Data mining algorithm is in need to organize and pattern the ASD details. The machine algorithms are available to classify the data in data mining works. In this proposed work, a machine learning algorithm called Support Vector Machine is used to classify the ASD children accurately. SVM is one of the classification algorithms which finding the hyper plane that maximizes the margin between the two classes. Though SVM give better identification of disease, some children have their unique nature which hides their problem of ASD easily. So, to diagnose the problem accurately, the user defined SVM parameters are tuned by optimization algorithm called Differential Evolutionary Algorithm. DE is an optimization algorithm used to find the optimal solution of SVM parameters. Further, to improve the performance of the proposed method, the dimension reduction technique is followed to reduce the SVM and ANN network dimension. The Sequential Feature Selection (SFS) method is applied in this paper, which select the most influenced variables for the output. The reduced network is further classified by ANN and SVM model. The Data set for the ANN and SVM network has been taken from the real records of the multi-specialty hospitals. The SVM and DE optimized SVM results are compared with another classification model called Artificial Neural Networks. The test results show the betterment of DE optimized SVM which give the classification of ASD child very accurately compare with ANN and DE optimized ANN.

scholarly journals Segmentation and Classification of Image Abnormalities in Retinal Fundus using Discrete Wavelet Transforms

2019 ◽  
Vol 8 (4) ◽  
pp. 11357-11360
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transforms ◽  
Region Of Interest ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Data Set ◽  
Retinal Fundus

Glaucoma disease diagnosis greatly based on the accurate retinal image segmentation and classification of images. Segmentation means to divide the images into a patchwork of regions, each of which is “homogeneous”, that is the “same” in some sense. Using discrete wavelet transform, the segmented images are classified by Support Vector Machine (SVM) classifiers to classify the Glaucoma images.The proposed Support Vector Machine classifier is used to extract the information rely on the Region of Interest (ROI) from original retinal fundus image. Thus the classification result are used to find the normal and abnormal image and also to compute the normal and abnormal accuracies.We observed an accuracy of around 93% using data set by SVM classifier.

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