Support Vector Machines (SVM) as a Technique for Solvency Analysis

Auria, Laura; Moro, Rouslan A.

doi:10.2139/ssrn.1424949

Cited by 268 publications

(168 citation statements)

References 6 publications

(3 reference statements)

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“…Parameter yang diperoleh dari hasil pelatihan dengan metode SVM dijamin adalah parameter optimal. Hal ini berbeda jika dibandingkan dengan metode Neural Network dimana bisa terjadi solusi yang diperoleh terjebak dalam minimum lokal [12].…”

Section: B Support Vector Machinesunclassified

“…Beberapa penelitian telah menggunakan SVM untuk berbagai penerapan, diantaranya adalah pada pengenalan citra, analisis medik, ataupun untuk melakukan prediksi. Secara spesifik, Wang merangkum beberapa penelitian yang berkaitan dengan perkembangan SVM beserta penggunaannya [12]. Dalam beberapa penelitian ditunjukkan bahwa SVM adalah metode yang efisien [13][14] [15].…”

Section: B Support Vector Machinesunclassified

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Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

Widiastuti

Rainarli²,

Dewi³

2017

INFOTEL

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Abstrak -Klasifikasi adalah proses pengelompokkan objek yang memiliki karakteristik atau ciri yang sama ke dalam beberapa kelas. Klasifikasi dokumen secara otomatis dapat dilakukan dengan menggunakan ciri atau fitur kata yang muncul pada dokumen latih. Jumlah dokumen yang besar dan banyak mengakibatkan jumlah kata yang muncul sebagai fitur akan bertambah. Oleh karena itu, peringkasan dipilih untuk mereduksi jumlah kata yang digunakan dalam proses klasifikasi. Untuk proses klasifikasi digunakan metode Support Vector Machine (SVM) untuk multikelas. SVM dipilih karena dianggap memiliki reputasi yang baik dalam klasifikasi. Penelitian ini menguji penggunaan ringkasan sebagai seleksi fitur dalam klasifikasi dokumen. Peringkasan menggunakan kompresi 50 %. Hasil yang diperoleh menunjukkan bahwa proses peringkasan tidak mempengaruhi nilai akurasi dari klasifikasi dokumen yang menggunakan SVM.Akan tetapi, penggunaan peringkasan berpengaruh pada peningkatan hasil akurasi dari metode klasifikasiSimple Logistic Classifier (SLC). Hasil pengujian metode klasifikasi menunjukkan bahwa penggunaan metode Naïve Bayes Multinomial (NBM) menghasilkan akurasi yang lebih baik dari pada metode SVM.Kata kunci -peringkasan, klasifikasi, SVM, seleksi fitur, dokumen Abstract -Classification is the process of grouping objects that have the same features or characteristics into several classes. The automatic documents classification use words frequency that appears on training data as features.The large number of documents cause the number of words that appears as a feature will increase.Therefore, summaries are chosen to reduce the number of words that used in classification.The classification uses multiclass Support Vector Machine (SVM) method.SVM was considered to have a good reputation in the classification. This research tests the effect of summary as selection features into documents classification.The summariesreduce text into 50%.A result obtained that the summaries did not affect value accuracy of classification of documents that use SVM.But, summaries improve the accuracy of Simple Logistic Classifier. The classification testing shows that the accuracy of Naïve Bayes Multinomial (NBM) better than SVM.

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Section: B Support Vector Machinesunclassified

Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

Widiastuti

Rainarli²,

Dewi³

2017

INFOTEL

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“…SVM has advantages, and as mentioned by [9] they are: SVM produces accurate result classification result on theoretical basis, even when input data are non-linearly separable. Also, the accuracy result does not rely on the quality of human expertise judgment for choice of the linearization function for non linear input data.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

“…A disadvantage of SVM as a non-parametric technique mentioned by [9] is its lack for transparency for results. The biggest limitation as mentioned by [10] is that which lies in the choice of the kernel.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Artificial Intelligence in Personalized Medicine Application of AI Algorithms in Solving Personalized Medicine Problems

Awwalu¹,

Garba²,

Ghazvini³

et al. 2015

IJCTE

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Abstract-ArtificialIntelligence has significantly gained grounds in our daily livelihood in this age of information and technology. As with any field of study, evolution takes place in terms of breakthrough or developmental research leading to advancement and friendly usability of that specific technology. Problems from different areas have been successfully solved using Artificial Intelligence algorithms. In order to use AI algorithms in solving Personalized Medicine problems such as; disease detection or prediction, accurate disease diagnosis, and treatment optimization, the choice of the algorithm influenced by its ability and applicability matters. This paper reviews the application and ability of artificial neural network (ANN), support vector machines (SVM), Naï ve Bayes, and fuzzy logic in solving personalized medicine problems, and shows that the obtained results meet expectations. Also, the achievement from the previous studies encourages developers and researchers to use these algorithms in solving Medical and Personalized Medicine problems.Index Terms-Personalized medicine, artificial neural networks, support vector machines, Naive Bayesian. I. INTRODUCTIONIt is always a surprising problem seeing a drug work for some people and be less effective on others, or causing side effects in another. Another problem is the question of why some people develop some diseases e.g. cancers, while others do not. Genetic make-up and other differential factors such as age; lifestyle could be reasons for these problems. As such, [1] believes medicine should approach each patient"s illness as unique, with medication tailored to the person"s history and biology. This approach to medical practice is known as Personalized or Precision Medicine.Patients with same diagnostics result must not be treated the same way; they can receive different treatment in order to achieve efficient treatment as illustrated in Fig. 1. Personalized medicine as a branch or extension of Medical Sciences uses practice and medical decisions to deliver customized healthcare service to patients. The major role of personalized medicine as posited by [2] is to predict the possibility of an individual developing a disease, achieve accurate diagnosis, and optimize the best treatment available.

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“…On the area of mining, classifying and predicting disaster-related tweets, one of the learning algorithms used to train an automatic classifier is Support Vector Machine (SVM). Classifier models produced by SVM give good classification results with high accuracy and robustness [2]. Although there are several studies involving the evaluation SVM-based classifiers that filter and predict disaster-related tweets, all of them only dealt with training and testing classifiers on a single-source domain.…”

mentioning

confidence: 99%

SVM-Based Domain Adaptation Machine Learned Models for the Automatic Classification of Disaster-Related Tweets

2017

IJCCIE

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Abstract-Twitter is a well-known microblogging service used by many users to quickly acquire disaster-related information. Since there is an influx of tweet during the occurrence of a disaster event, the need to automatically filter informative disaster-related tweets is imperative. In this study, the researchers used the Habagat tweets and tweets gathered during the Zamboanga Siege event as training corpora for training two Support Vector Machine (SVM) machine classifiers. The first machine classifier is used to filter informative tweets while the second model is used to classify informative tweets as Donation, Traffic update, Weather update, Class suspension, Rescue and Relief and River level/condition. These subclasses were based on the latent topics extracted from the corpus of informative tweets using Principal Component Analysis(PCA) and Latent Dirichlet Allocation(LDA). Several experiments using different preprocessing techniques, vector space of unigrams/bigrams with TF-IDF weightings on balanced and unbalanced data were applied to test and train the classifiers. SVM and Expectation Maximization (EM) algorithms were used for the training and testing of the machine classifiers. The models were evaluated based on the performance metrics of accuracy, precision and recall. Experimental results show that for a binary classifier, using unigram with a corpus of unbalanced labeled corpus returned best classification results and applying bigram to a balanced corpus gave high performance results. Furthermore, the multiclass model yielded better performance results with unigrams as features.

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Support Vector Machines (SVM) as a Technique for Solvency Analysis

Cited by 268 publications

References 6 publications

Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

Artificial Intelligence in Personalized Medicine Application of AI Algorithms in Solving Personalized Medicine Problems

SVM-Based Domain Adaptation Machine Learned Models for the Automatic Classification of Disaster-Related Tweets

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