scholarly journals KLASIFIKASI DIABETIC RETINOPATHY MENGGUNAKAN SELEKSI FITUR DAN SUPPORT VECTOR MACHINE

2018 ◽  
Vol 1 (2) ◽  
pp. 109-117
Author(s):  
Muhammad Imron Rosadi ◽  
Cahya Bagus Sanjaya ◽  
Lukman Hakim
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Feature Selection ◽  
Diabetic Retinopathy ◽  
Selection Process ◽  
Computational Time ◽  
Support Vector ◽  
Features Selection ◽  
Kernel Parameters ◽  
Selection Of

Diabetic Retinopathy is a disease common complications of diabetes mellitus. The complications in the form of damages on the part of the retina of the eye.  The high levels of glucose in the blood are the cause of small capillaries become broke and can lead to blindness. The symptoms shown by the sufferers of Diabetic Retinopaythy (DR), among others, microaneurysms, hemorrhages, exudates, soft hard exudate and neovascularization. These symptoms are at a certain intensity can be an indicator of the phase (the level of severity) DR sufferers. There are four stages of the process of pattern recognition, namely preprocessing,feature ekstraction, feature selection and classification. On preprocessing the image do Change the RGB image into Green channel, image Adaptive Histogram Equalization, removal of blood vessels, removal of optic disks, detection of exudate. A collection from the results of preprocessing placed in the vector of characteristics by using the feature extraction of GLCM consisting of order 1 and 2, to order then conducted as input Support Vector Machine (SVM). While in SVM there are three issues that emerged, namely; How to select a kernel function, what is the optimal number of input features, and how to determine the best kernel parameters. These issues are important, because the number of features affect the required kernel parameters values and vice versa, so that the selection of the features required in building the classification system. On the research of feature extraction methods was presented GLCM, features selection, and SVM for detecting diabetic retinopathy. feature selection process using the F-Score feature to select the results of features extraction. From the results of the selection of these features is used to input the classification. The dataset used amounted to 50 data, which is divided into 2 classes, where 25 sets taken from normal retinal scans and 25 sets of the rest of the scan of the retina with diabetic retinopathy. SVM classification with feature selection to increase accuracy and computational time than lose without a selection of features with a value of 90% accuracy and computational time 0.010 seconds.

KLASIFIKASI MASSA PADA CITRA MAMMOGRAM MENGGUNAKAN KOMBINASI SELEKSI FITUR F-SCORE DAN LS-SVM

Teknologi ◽  
2016 ◽  
Vol 6 (1) ◽  
pp. 27
Author(s):  
Muhammad I. Rosadi ◽  
Agus Z. Arifin ◽  
Anny Yuniarti
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Feature Selection ◽  
Support Vector ◽  
Gray Level ◽  
Computer Aided Detection ◽  
Computer Aided ◽  
Occurrence Matrix ◽  
Kernel Parameters

ABSTRAKKanker payudara adalah penyakit yang paling umum diderita oleh perempuan pada banyak negara. Pemeriksaan kanker payudara dapat dilakukan menggunakan citra Mammogram dengan teknologi sistem Computer-Aided Detection (CAD). Analisis CAD yang telah dikembangkan adalah ekstraksi fitur GLCM, reduksi/seleksi fitur, dan SVM. Pada SVM (Support Vector Machine) maupun LS-SVM (Least Square Support Vector Machine) terdapat tiga masalah yang muncul, yaitu: Bagaimana memilih fungsi kernel, berapa jumlah fitur input yang dioptimalkan, dan bagaimana menentukan parameter kernel terbaik. Jumlah fitur dan nilai parameter kernel yang diperlukan saling mempengaruhi, sehingga seleksi fitur diperlukan dalam membangun sistem klasifikasi. Pada penelitian ini bertujuan untuk mengklasifikasi massa pada citra Mammogram berdasarkan dua kelas yaitu kelas kanker jinak dan kelas kanker ganas. Ekstraksi fitur menggunakan Gray Level Co-occurrence Matrix (GLCM). Hasil proses ekstraksi fitur tersebut kemudian diseleksi mengunakan metode F-Score. F-Score diperoleh dengan menghitung nilai diskriminan data hasil ekstraksi fitur di antara data dua kelas pada data training. Nilai F-Score masing-masing fitur kemudian diurutkan secara descending. Hasil pengurutan tersebut digunakan untuk membuat kombinasi fitur. Kombinasi fitur tersebut digunakan sebagai input LS-SVM. Dari hasil uji coba penelitian ini didapatkan, bahwa menggunakan kombinasi seleksi fitur sangat berpengaruh terhadap tingkat akurasi. Akurasi terbaik didapat dengan menggunakan LS-SVM RBF dan SVM RBF baik dengan kombinasi seleksi fitur, maupun tanpa kombinasi seleksi fitur dengan nilai akurasi yaitu 97,5%. Selain itu juga seleksi fitur mampu mengurangi waktu komputasi.Kata Kunci: F-Score, GLCM, kanker payudara, LS-SVM.ABSTRACTBreast cancer is the most common disease suffered by women in many countries. Breast cancer screening can be done using a mammogram image. Computer-aided detection system (CAD). CAD analysis that has been developed is GLCM efficient feature extraction, reduction / feature selection and SVM. In SVM (Support Vector Machine) and LS-SVM (Support Vector Machine Square least) there are three problems that arise, namely; how to choose the kernel function, how many input fea-tures are optimal, and how to determine the best kernel parameters. The number of fea-tures and value required kernel parameters affect each other, so that the selection of the features needed to build a system of classification. In this study aims to classify image of masses on digital mammography based on two classes benign cancer and malignant cancer. Feature extraction using gray level co-occurrence matrix (GLCM). The results of the feature extraction process then selected using the method F-Score. F-Score is obtained by calculating the value of the discriminant feature extraction results data between two classes of data in the data training. Value F-Score of each feature and then sorted in descending order. The sequenc-ing results are used to make the combination of fea-tures. The combination of these features are used as input LS-SVM. From the experiments that use a combination of feature selection affects the accuracy ting-kat. Best accuracy obtained using LS-SVM and SVM RBF RBF with combi-nation or without the combination of feature selection with accuracy value is 97.5%. It also features a selection able to curate the computa-tion time.Keywords: Breast Cancer, F-Score, GLCM, LS-SVM.

Selection of Wavelet Features for Biomedical Signals Using SVM Learning

2016 ◽  
pp. 299-308
Author(s):  
Girisha Garg ◽  
Vijander Singh
Keyword(s):  
Support Vector Machine ◽  
Signal Processing ◽  
Feature Extraction ◽  
Feature Selection ◽  
Feature Space ◽  
Support Vector ◽  
Feature Extraction And Selection ◽  
Wavelet Features ◽  
Wavelet Decompositions ◽  
Selection Of

Signal processing problems require feature extraction and selection techniques. A novel Wavelet Feature Selection algorithm is proposed for ranking and selecting the features from the wavelet decompositions. The algorithm makes use of support vector machine to rank the features and backward feature elimination to remove the features. The finally selected features are used as patterns for the classification system. Two EEG datasets are used to test the algorithm. The results confirm that the algorithm is able to improve the efficiency of wavelet features in terms of accuracy and feature space.

Hybrid adapted fast correlation FCBF-support vector machine recursive feature elimination for feature selection

2020 ◽  
Vol 14 (3) ◽  
pp. 269-279
Author(s):  
Hayet Djellali ◽  
Nacira Ghoualmi-Zine ◽  
Souad Guessoum
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Svm Classifier ◽  
Hybrid Architecture ◽  
Features Selection ◽  
K Nearest Neighbors ◽  
Correlation Based Feature Selection ◽  
Embedded Method

This paper investigates feature selection methods based on hybrid architecture using feature selection algorithm called Adapted Fast Correlation Based Feature selection and Support Vector Machine Recursive Feature Elimination (AFCBF-SVMRFE). The AFCBF-SVMRFE has three stages and composed of SVMRFE embedded method with Correlation based Features Selection. The first stage is the relevance analysis, the second one is a redundancy analysis, and the third stage is a performance evaluation and features restoration stage. Experiments show that the proposed method tested on different classifiers: Support Vector Machine SVM and K nearest neighbors KNN provide a best accuracy on various dataset. The SVM classifier outperforms KNN classifier on these data. The AFCBF-SVMRFE outperforms FCBF multivariate filter, SVMRFE, Particle swarm optimization PSO and Artificial bees colony ABC.

scholarly journals Support Vector Machine Based Feature Extraction For Gender Recognition From Objects Using Lasso Classifier

2020 ◽  
Author(s):  
Damodara Krishna Kishore Galla ◽  
BabuReddy Mukamalla ◽  
Rama Prakasha Reddy Chegireddy
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Face Recognition ◽  
Selection Process ◽  
Recognition System ◽  
Classification Model ◽  
Support Vector ◽  
Gender Classification ◽  
Accuracy Score ◽  
Lasso Regression

Abstract The blind people has their difficulty to identify the object moving around them, therefore with a high accuracy score object detection and human face recognition system will helps them in identifying the things around them with ease. Facial record images are immobile an difficult assignment for biometric authentication systems due to various types of characteristics are dimensions, pose, expressions, illustrations and age etc. In facial and other united images includes different objects classifications. In this research article, a minimum distance trainer for feature selection by accessing SVM feature optimization process. For feature selection process SVM (support vector machine) was considered for improving its feature interpretability and computational efficiency., then LASSO classifier applied to perform object recognition and gender classification. Original face image database used for the gender classification. This approach was implemented with dual classification model (1) Recognizing or classifying human faces from various objects and (2) Classifying gender through face recognition] is made possible with the help of combining modified SIFT feature in combination with ridge regression (RR), elastic net (EN), lasso regression(LR) and lasso regression with Gaussian Support Vector Machines (LRGS) based classification.

Implementasi teknik seleksi fitur pada klasifikasi malware Android menggunakan support vector machine (SVM)

Repositor ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Hendra Saputra ◽  
Setio Basuki ◽  
Mahar Faiqurahman
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Chi Square ◽  
Android Malware ◽  
Correlation Based Feature Selection ◽  
Selection Of

AbstrakPertumbuhan Malware Android telah meningkat secara signifikan seiring dengan majunya jaman dan meninggkatnya keragaman teknik dalam pengembangan Android. Teknik Machine Learning adalah metode yang saat ini bisa kita gunakan dalam memodelkan pola fitur statis dan dinamis dari Malware Android. Dalam tingkat keakurasian dari klasifikasi jenis Malware peneliti menghubungkan antara fitur aplikasi dengan fitur yang dibutuhkan dari setiap jenis kategori Malware. Kategori jenis Malware yang digunakan merupakan jenis Malware yang banyak beredar saat ini. Untuk mengklasifikasi jenis Malware pada penelitian ini digunakan Support Vector Machine (SVM). Jenis SVM yang akan digunakan adalah class SVM one against one menggunakan Kernel RBF. Fitur yang akan dipakai dalam klasifikasi ini adalah Permission dan Broadcast Receiver. Untuk meningkatkan akurasi dari hasil klasifikasi pada penelitian ini digunakan metode Seleksi Fitur. Seleksi Fitur yang digunakan ialah Correlation-based Feature  Selection (CSF), Gain Ratio (GR) dan Chi-Square (CHI). Hasil dari Seleksi Fitur akan di evaluasi bersama dengan hasil yang tidak menggunakan Seleksi Fitur. Akurasi klasifikasi Seleksi Fitur CFS menghasilkan akurasi sebesar 90.83% , GR dan CHI sebesar 91.25% dan data yang tidak menggunakan Seleksi Fitur sebesar 91.67%. Hasil dari pengujian menunjukan bahwa Permission dan Broadcast Receiver bisa digunakan dalam mengklasifikasi jenis Malware, akan tetapi metode Seleksi Fitur yang digunakan mempunyai akurasi yang berada sedikit dibawah data yang tidak menggunakan Seleksi Fitur. Kata kunci: klasifikasi malware android, seleksi fitur, SVM dan multi class SVM one agains one  Abstract Android Malware has growth significantly along with the advance of the times and the increasing variety of technique in the development of Android. Machine Learning technique is a method that now we can use in the modeling the pattern of a static and dynamic feature of Android Malware. In the level of accuracy of the Malware type classification, the researcher connect between the application feature with the feature required by each types of Malware category. The category of malware used is a type of Malware that many circulating today, to classify the type of Malware in this study used Support Vector Machine (SVM). The SVM type wiil be used is class SVM one against one using the RBF Kernel. The feature will be used in this classification are the Permission and Broadcast Receiver.  To improve the accuracy of the classification result in this study used Feature Selection method. Selection of feature used are Correlation-based Feature Selection (CFS), Gain Ratio (GR) and Chi-Square (CHI). Result from Feature Selection will be evaluated together with result that not use Feature Selection. Accuracy Classification Feature Selection CFS result accuracy of 90.83%, GR and CHI of 91.25% and data that not use Feature Selection of 91.67%. The result of testing indicate that permission and broadcast receiver can be used in classyfing type of Malware, but the Feature Selection method that used have accuracy is a little below the data that are not using Feature Selection. Keywords: Classification Android Malware, Feature Selection, SVM and Multi Class SVM one against one

scholarly journals A Hybrid Fish – Bee Optimization Algorithm for Heart Disease Prediction using Multiple Kernel SVM Classifier

2019 ◽  
Vol 8 (9S2) ◽  
pp. 729-737
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Heart Disease ◽  
Optimization Algorithm ◽  
Selection Process ◽  
Support Vector ◽  
Svm Classifier ◽  
Disease Prediction ◽  
Kernel Support Vector Machine ◽  
Hybrid Fish

The patient’s heart disease status is obtained by using a heart disease detection model. That is used for the medical experts. In order to predict the heart disease, the existing technique use optimal classifier. Even though the existing technique achieved the better result, it has some disadvantages. In order to improve those drawbacks, the suggested technique utilizes the effective method for heart disease prediction. At first the input information is preprocessed and then the preprocessed result is forwarded to the feature selection process. For the feature selection process a proficient feature selection is used over the high dimensional medical data. Hybrid Fish Bee optimization algorithm (HFSBEE) is utilized. Thus, the proposed algorithm parallelizes the two algorithms such that the local behavior of artificial bee colony algorithm and global search of fish swarm optimization are effectively used to find the optimal solution. Classification process is performed by the transformation of medical dataset to the Multi kernel support vector machine (MKSVM). The process of our proposed technique is calculated based on the accuracy, sensitivity, specificity, precision, recall and F-measure. Here, for test analysis, the some datasets used i.e. Cleveland, Hungarian and Switzerland etc., that are given based on the UCI machine learning repository. The experimental outcome show that our presented technique is went better than the accuracy of 97.68%. This is for the Cleveland dataset when related with existing hybrid kernel support vector machine (HKSVM) method achieved 96.03% and optimal rough fuzzy classifier obtained 62.25%. The implementation of the proposed method is done by MATLAB platform.

scholarly journals Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification

2021 ◽  
Vol 18 (4) ◽  
pp. 2197
Author(s):  
Kwang Ho Park ◽  
Erdenebileg Batbaatar ◽  
Yongjun Piao ◽  
Nipon Theera-Umpon ◽  
Keun Ho Ryu
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Feature Selection ◽  
Classification Model ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Subtype Classification ◽  
Cancer Subtype ◽  
Hematopoietic Cancer

Hematopoietic cancer is a malignant transformation in immune system cells. Hematopoietic cancer is characterized by the cells that are expressed, so it is usually difficult to distinguish its heterogeneities in the hematopoiesis process. Traditional approaches for cancer subtyping use statistical techniques. Furthermore, due to the overfitting problem of small samples, in case of a minor cancer, it does not have enough sample material for building a classification model. Therefore, we propose not only to build a classification model for five major subtypes using two kinds of losses, namely reconstruction loss and classification loss, but also to extract suitable features using a deep autoencoder. Furthermore, for considering the data imbalance problem, we apply an oversampling algorithm, the synthetic minority oversampling technique (SMOTE). For validation of our proposed autoencoder-based feature extraction approach for hematopoietic cancer subtype classification, we compared other traditional feature selection algorithms (principal component analysis, non-negative matrix factorization) and classification algorithms with the SMOTE oversampling approach. Additionally, we used the Shapley Additive exPlanations (SHAP) interpretation technique in our model to explain the important gene/protein for hematopoietic cancer subtype classification. Furthermore, we compared five widely used classification algorithms, including logistic regression, random forest, k-nearest neighbor, artificial neural network and support vector machine. The results of autoencoder-based feature extraction approaches showed good performance, and the best result was the SMOTE oversampling-applied support vector machine algorithm consider both focal loss and reconstruction loss as the loss function for autoencoder (AE) feature selection approach, which produced 97.01% accuracy, 92.60% recall, 99.52% specificity, 93.54% F1-measure, 97.87% G-mean and 95.46% index of balanced accuracy as subtype classification performance measures.

scholarly journals Perbandingan Simple Logistic Classifier dengan Support Vector Machine dalam Memprediksi Kemenangan Atlet

2017 ◽  
Vol 3 (2) ◽  
pp. 87 ◽  
Author(s):  
Ednawati Rainarli ◽  
Arif Romadhan
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Selection Process ◽  
Support Vector ◽  
Good Prospect ◽  
Features Selection ◽  
Processing Times ◽  
Simple Logistic ◽  
Sports Data

Abstrak— Prediksi kemenangan atlet adalah hal yang harus dilakukan oleh pelatih ketika memutuskan pemain  yang akan diturunkan dalam suatu pertandingan. Banyaknya faktor-faktor yang mempengaruhi kemenangan atlet membuat keputusan tersebut tidak mudah untuk ditentukan. Dalam penelitian ini akan dilakukan perbandingan dari penggunaan metode Simple Logistic Classifier (SLC) dengan Support Vector Machine (SVM)  dalam memprediksi kemenangan atlet berdasarkan data kesehatan dan data latihan fisik. Data yang digunakan diambil dari 28 cabang olahraga perorangan. Rata-rata akurasi SLC dan SVM masing-masing diperoleh sebesar 80% dan 88%, sedangkan rata-rata kecepatan pemrosesan metode SLC dan SVM adalah 1,6 detik dan 0,2 detik.  Hal ini menunjukkan bahwa penggunaan metode SVM lebih unggul daripada SLC, baik dari segi kecepatan maupun dari nilai akurasi yang dihasilkan. Selain pengujian akurasi, dilakukan pula pengujian terhadap 24 fitur yang digunakan dalam proses klasifikasi.  Hasilnya diketahui bahwa pengurangan fitur melalui tahap seleksi mengakibatkan penurunan nilai akurasi. Berdasarkan hal tersebut disimpulkan bahwa semua fitur yang digunakan dalam penelitian ini adalah fitur yang berpengaruh dalam penentuan prediksi kemenangan atlet. Kata Kunci— Prediksi, Simple Logistic Classifier, Sports Data Mining, Support Vector MachineAbstract— A coach must be able to select which athlete has a good prospect of winning a game.  There are a lot of aspects which influence the athlete in winning a game, so it's not easy by coach to decide it.This research would compare Simple Logistic Classifier (SLC) and Support Vector Machine (SVM) usage applied to predict winning game of athlete based on health and physical condition record.  The data get from 28 sports. The accuracy of SLC and SVM are 80% and 88% meanwhile processing times of SLC and SVM method are 1.6 seconds dan 0.2 seconds.The result shows the SVM usage superior to the SLC both of speed process and the value of accuracy.  There were also testing of 24 features used in the classifications process. Based on the test,  features selection process can cause decreasing the accuracy value. This result concludes that all features used in this research influence the determination of a victory athletes prediction. Keywords— Prediction, Simple Logistic Classifier, Sports Data Mining, Support Vector Machine

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