Performance improvement of Support Vector Machine (SVM) With information gain on categorization of Indonesian news documents

This study aims to clarify tweets on twitter using the Support Vector Machine and Information Gain methods. The clarification itself aims to find a hyperplane that separates the negative and positive classes. In the research stage, there is a system process, namely text mining, text processing which has stages of tokenizing, filtering, stemming, and term weighting. After that, a feature selection is made by information gain which calculates the entropy value of each word. After that, clarify based on the features that have been selected and the output is in the form of identifying whether the tweet is bully or not. The results of this study found that the Support Vector Machine and Information Gain methods have sufficiently maximum results.

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ITERATIVE FEATURE PERTURBATION AS A GENE SELECTOR FOR MICROARRAY DATA

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001412600038 ◽

2012 ◽

Vol 26 (05) ◽

pp. 1260003 ◽

Cited By ~ 16

Author(s):

JUANA CANUL-REICH ◽

LAWRENCE O. HALL ◽

DMITRY B. GOLDGOF ◽

JOHN N. KORECKI ◽

STEVEN ESCHRICH

Keyword(s):

Gene Expression ◽

Colon Cancer ◽

Support Vector Machine ◽

Performance Improvement ◽

Microarray Data ◽

T Test ◽

Recursive Feature Elimination ◽

Support Vector ◽

Gene Sets ◽

Microarray Datasets

Gene-expression microarray datasets often consist of a limited number of samples with a large number of gene-expression measurements, usually on the order of thousands. Therefore, dimensionality reduction is critical prior to any classification task. In this work, the iterative feature perturbation method (IFP), an embedded gene selector, is introduced and applied to four microarray cancer datasets: colon cancer, leukemia, Moffitt colon cancer, and lung cancer. We compare results obtained by IFP to those of support vector machine-recursive feature elimination (SVM-RFE) and the t-test as a feature filter using a linear support vector machine as the base classifier. Analysis of the intersection of gene sets selected by the three methods across the four datasets was done. Additional experiments included an initial pre-selection of the top 200 genes based on their p values. IFP and SVM-RFE were then applied on the reduced feature sets. These results showed up to 3.32% average performance improvement for IFP across the four datasets. A statistical analysis (using the Friedman/Holm test) for both scenarios showed the highest accuracies came from the t-test as a filter on experiments without gene pre-selection. IFP and SVM-RFE had greater classification accuracy after gene pre-selection. Analysis showed the t-test is a good gene selector for microarray data. IFP and SVM-RFE showed performance improvement on a reduced by t-test dataset. The IFP approach resulted in comparable or superior average class accuracy when compared to SVM-RFE on three of the four datasets. The same or similar accuracies can be obtained with different sets of genes.

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A Support Vector Machine and Information Gain based Classification Framework for Diabetic Retinopathy Images

International Journal of Computer Trends and Technology ◽

10.14445/22312803/ijctt-v8p112 ◽

2014 ◽

Vol 8 (2) ◽

pp. 65-69

Author(s):

M. Dharani ◽

◽

T. Menaka ◽

G. Vinodhini

Keyword(s):

Support Vector Machine ◽

Diabetic Retinopathy ◽

Information Gain ◽

Support Vector ◽

Classification Framework

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Cerebral infarction classification using multiple support vector machine with information gain feature selection

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i4.1997 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1578-1584

Author(s):

Zuherman Rustam ◽

Arfiani Arfiani ◽

Jacub Pandelaki

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Cerebral Infarction ◽

Information Gain ◽

The Elderly ◽

Common Type ◽

Support Vector ◽

Classification Result ◽

The Third ◽

Multiple Support

Stroke ranks the third leading cause of death in the world after heart disease and cancer. It also occupies the first position as a disease that causes both mild and severe disability. The most common type of stroke is cerebral infarction, which increases every year in Indonesia. This disease does not only occur in the elderly, but in young and productive people which makes early detection very important. Although there are varied of medical methods used to classify cerebral infarction, this study uses a multiple support vector machine with information gain feature selection (MSVM-IG). MSVM-IG is a modification among IG Feature Selection and SVM, where SVM conducted doubly in the process of classification which utilizes the support vector as a new dataset. The data obtained from Cipto Mangunkusumo Hospital, Jakarta. Based on the results, the proposed method was able to achieve an accuracy value of 81%, therefore, this method can be considered to use for better classification result.

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Performance Comparison of Oil Spill and Ship Classification from X-Band Dual- and Single-Polarized SAR Image Using Support Vector Machine, Random Forest, and Deep Neural Network

Remote Sensing ◽

10.3390/rs13163203 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3203

Author(s):

Won-Kyung Baek ◽

Hyung-Sup Jung

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Random Forest ◽

Performance Improvement ◽

Oil Spill ◽

Deep Neural Network ◽

Support Vector ◽

Sar Image ◽

X Band

It is well known that the polarization characteristics in X-band synthetic aperture radar (SAR) image analysis can provide us with additional information for marine target classification and detection. Normally, dual-and single-polarized SAR images are acquired by SAR satellites, and then we must determine how accurate the marine mapping performance from dual-polarized (pol) images is versus the marine mapping performance from the single-pol images in a given machine learning model. The purpose of this study is to compare the performance of single- and dual-pol SAR image classification achieved by the support vector machine (SVM), random forest (RF), and deep neural network (DNN) models. The test image is a TerraSAR-X dual-pol image acquired from the 2007 Kerch Strait oil spill event. For this, 824,026 pixels and 1,648,051 pixels were extracted from the image for the training and test, respectively, and sea, ship, oil, and land objects were classified from the image by using the three machine learning methods. The mean f1-scores of the SVM, RF, and DNN models resulting from the single-pol image were approximately 0.822, 0.882, and 0.889, respectively, and those from the dual-pol image were about 0.852, 0.908, and 0.898, respectively. The performance improvement achieved by dual-pol was about 3.6%, 2.9%, and 1% in SVM, RF, and DNN, respectively. The DNN model had the best performance (0.889) in the single-pol test while the RF model was best (0.908) in the dual-pol test. The performance improvement was approximately 2.1% and not noticeable. If the condition that dual-pol images have two-times lower spatial resolution versus single-pol images in the azimuth direction is considered, a small improvement may not be valuable. Therefore, the results show that the performance improvement by X-band dual-pol image may be not remarkable when classifying the sea, ships, oil spills, and sea and land surfaces.

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Support Vector Machine Berbasis Feature Selection Untuk Sentiment Analysis Kepuasan Pelanggan Terhadap Pelayanan Warung dan Restoran Kuliner Kota Tegal

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.201855867 ◽

2018 ◽

Vol 5 (5) ◽

pp. 537 ◽

Cited By ~ 1

Author(s):

Oman Somantri ◽

Dyah Apriliani

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Sentiment Analysis ◽

Information Gain ◽

Support Vector ◽

Chi Square ◽

Proposed Model ◽

Chi Squared ◽

The Difference ◽

Increase In Accuracy

Abstrak Setiap pelanggan pasti menginginkan sebuah pendukung keputusan dalam menentukan pilihan ketika akan mengunjungi sebuah tempat makan atau kuliner yang sesuai dengan keinginan salah satu contohnya yaitu di Kota Tegal. Sentiment analysis digunakan untuk memberikan sebuah solusi terkait dengan permasalahan tersebut, dengan menereapkan model algoritma Support Vector Machine (SVM). Tujuan dari penelitian ini adalah mengoptimalisasi model yang dihasilkan dengan diterapkannya feature selection menggunakan algoritma Informatioan Gain (IG) dan Chi Square pada hasil model terbaik yang dihasilkan oleh SVM pada klasifikasi tingkat kepuasan pelanggan terhadap warung dan restoran kuliner di Kota Tegal sehingga terjadi peningkatan akurasi dari model yang dihasilkan. Hasil penelitian menunjukan bahwa tingkat akurasi terbaik dihasilkan oleh model SVM-IG dengan tingkat akurasi terbaik sebesar 72,45% mengalami peningkatan sekitar 3,08% yang awalnya 69.36%. Selisih rata-rata yang dihasilkan setelah dilakukannya optimasi SVM dengan feature selection adalah 2,51% kenaikan tingkat akurasinya. Berdasarkan hasil penelitian bahwa feature selection dengan menggunakan Information Gain (IG) (SVM-IG) memiliki tingkat akurasi lebih baik apabila dibandingkan SVM dan Chi Squared (SVM-CS) sehingga dengan demikian model yang diusulkan dapat meningkatkan tingkat akurasi yang dihasilkan oleh SVM menjadi lebih baik. Abstract The Customer needs to get a decision support in determining a choice when they’re visit a culinary restaurant accordance to their wishes especially at Tegal City. Sentiment analysis is used to provide a solution related to this problem by applying the Support Vector Machine (SVM) algorithm model. The purpose of this research is to optimize the generated model by applying feature selection using Informatioan Gain (IG) and Chi Square algorithm on the best model produced by SVM on the classification of customer satisfaction level based on culinary restaurants at Tegal City so that there is an increasing accuracy from the model. The results showed that the best accuracy level produced by the SVM-IG model with the best accuracy of 72.45% experienced an increase of about 3.08% which was initially 69.36%. The difference average produced after SVM optimization with feature selection is 2.51% increase in accuracy. Based on the results of the research, the feature selection using Information Gain (SVM-IG) has a better accuracy rate than SVM and Chi Squared (SVM-CS) so that the proposed model can improve the accuracy of SVM better.

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Ensemble swarm behaviour based feature selection and support vector machine classifier for chronic kidney disease prediction

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.31.13438 ◽

2018 ◽

Vol 7 (2.31) ◽

pp. 190 ◽

Cited By ~ 1

Author(s):

S Belina V.J. Sara ◽

K Kalaiselvi

Keyword(s):

Chronic Kidney Disease ◽

Support Vector Machine ◽

Feature Selection ◽

Kidney Disease ◽

Information Gain ◽

Clonal Selection ◽

Prediction Algorithm ◽

Support Vector ◽

Svm Classifier ◽

Classification Algorithms

Kidney Disease and kidney failure is the one of the complicated and challenging health issues regarding human health. Without having any symptoms few diseases are detected in later stages which results in dialysis. Advanced excavating technologies can always give various possibilities to deal with the situation by determining important realations and associations in drilling down health related data. The prediction accuracy of classification algorithms depends upon appropriate Feature Selection (FS) algorithms decrease the number of features from collection of data. FS is the procedure of choosing the most relevant features, removing irrelevant features. To identify the Chronic Kidney Disease (CKD), Hybrid Wrapper and Filter based FS (HWFFS) algorithm is proposed to reduce the dimension of CKD dataset. Filter based FS algorithm is performed based on the three major functions: Information Gain (IG), Correlation Based Feature Selection (CFS) and Consistency Based Subset Evaluation (CS) algorithms respectively. Wrapper based FS algorithm is performed based on the Enhanced Immune Clonal Selection (EICS) algorithm to choose most important features from the CKD dataset. The results from these FS algorithms are combined with new HWFFS algorithm using classification threshold value. Finally Support Vector Machine (SVM) based prediction algorithm be proposed in order to predict CKD and being evaluated on the MATLAB platform. The results demonstrated with the purpose of the SVM classifier by using HWFFS algorithm provides higher prediction rate in the diagnosis of CKD when compared to other classification algorithms.

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Improved Accuracy of Sentiment Analysis Movie Review Using Support Vector Machine Based Information Gain

Journal of Physics Conference Series ◽

10.1088/1742-6596/1641/1/012060 ◽

2020 ◽

Vol 1641 ◽

pp. 012060

Author(s):

Reza Maulana ◽

Panny Agustia Rahayuningsih ◽

Windi Irmayani ◽

Dedi Saputra ◽

Wanty Eka Jayanti

Keyword(s):

Support Vector Machine ◽

Sentiment Analysis ◽

Information Gain ◽

Support Vector ◽

Improved Accuracy

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Penerapan SVM dan Information Gain Pada Analisis Sentimen Pelaksanaan Pilkada Saat Pandemi

Jurnal Teknologi Informatika dan Komputer ◽

10.37012/jtik.v7i2.641 ◽

2021 ◽

Vol 7 (2) ◽

pp. 101-109

Author(s):

Aliffia Kulsumarwati ◽

Intan Purnamasari ◽

Budi Arif Darmawan

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Information Gain ◽

Support Vector

Sosial media pada masa kini banyak dimanfaatkan untuk berbagai aktifitas, salah satunya adalah untuk menumpahkan segala tanggapannya terhadap kejadian-kejadian yang tengah terjadi di masyarakat. Seperti banyaknya masyarakat yang memberikan tanggapan terhadap kebijakan pemerintah Indonesia mengenai perlaksanaan Pilkada 2020 yang tetap diselenggarakan meski di tengah pandemi Covid-19 di Twitter. Berbagai tanggapan masyarakat ada yang mendukung maupun tidak setuju dengan diadakannya pilkada 2020 karna dilaksanakan di masa pandemi. Untuk itu maka dilakukan penerapan data mining dengan algoritma Support Vector Machine dan seleksi fitur information gain untuk menganalisis berbagai tanggapan masyarakat mengenai pelaksanaan pilkada 2020. Data yang digunakan merupakan tweet dari aplikasi Twitter sebanyak 496 data. Sebelum tahap data mining, dilakukan pembagian data menjadi 80% data traning dan 20% data testing. Hasil klasifikasi data tweet dengan Support Vector Machine menggunakan kernel linear menghasilkan nilai akurasi yang besar yaitu 92%, precision 90%, dan recall 92%.

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