Comparative Performance Analysis of Support Vector Machine, Random Forest, Logistic Regression and k-Nearest Neighbours in Rainbow Trout (Oncorhynchus Mykiss) Classification Using Image-Based Features

Public responses, posted on Twitter reacting to the Tokopedia data leak incident, were used as a data set to compare the performance of three different classifiers, trained using supervised learning modeling, to classify sentiment on the text. All tweets were classified into either positive, negative, or neutral classes. This study compares the performance of Random Forest, Support-Vector Machine, and Logistic Regression classifier. Data was scraped automatically and used to evaluate several models; the SVM-based model has the highest f1-score 0.503583. SVM is the best performing classifier.

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Predição de sinistros agrícolas: uma abordagem comparativa utilizando aprendizagem de máquina

Economia Aplicada ◽

10.11606/1980-5330/ea161194 ◽

2020 ◽

Vol 24 (4) ◽

pp. 533-554

Author(s):

Arthur Lula Mota ◽

Daniel Lima Miquelluti ◽

Vitor Augusto Ozaki

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Random Forest ◽

Support Vector ◽

Nearest Neighbours

O seguro agrícola tem ganho maior atenção no Brasil desde o início da década passada, com a implementação do Programa de Subvenção ao Prêmio do Seguro Rural. O presente estudo testou o desempenho de algoritmos de Machine Learning para as seguradoras anteciparem a ocorrência de sinistro, elaborando previsões por meio de dados de apólices e bases de dados climáticas entre os anos de 2006 e 2017. Foram testados os algoritmos Random Forest, Support Vector Machine e k-Nearest Neighbours. O segundo mostrou melhor performance preditiva de sinistros. No entanto, todos os métodos apresentaram baixa capacidade preditiva para a ocorrência de sinistros.

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IDENTIFIKASI JENIS IKAN MENGGUNAKAN MODEL HYBRID DEEP LEARNING DAN ALGORITMA KLASIFIKASI

Sebatik ◽

10.46984/sebatik.v24i2.1057 ◽

2020 ◽

Vol 24 (2) ◽

Author(s):

Anifuddin Azis

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Logistic Regression ◽

Deep Learning ◽

Random Forest ◽

Decision Tree ◽

Nearest Neighbor ◽

Support Vector ◽

K Nearest Neighbor ◽

Data Output

Indonesia merupakan negara dengan keanekaragaman hayati terbesar kedua di dunia setelah Brazil. Indonesia memiliki sekitar 25.000 spesies tumbuhan dan 400.000 jenis hewan dan ikan. Diperkirakan 8.500 spesies ikan hidup di perairan Indonesia atau merupakan 45% dari jumlah spesies yang ada di dunia, dengan sekitar 7.000an adalah spesies ikan laut. Untuk menentukan berapa jumlah spesies tersebut dibutuhkan suatu keahlian di bidang taksonomi. Dalam pelaksanaannya mengidentifikasi suatu jenis ikan bukanlah hal yang mudah karena memerlukan suatu metode dan peralatan tertentu, juga pustaka mengenai taksonomi. Pemrosesan video atau citra pada data ekosistem perairan yang dilakukan secara otomatis mulai dikembangkan. Dalam pengembangannya, proses deteksi dan identifikasi spesies ikan menjadi suatu tantangan dibandingkan dengan deteksi dan identifikasi pada objek yang lain. Metode deep learning yang berhasil dalam melakukan klasifikasi objek pada citra mampu untuk menganalisa data secara langsung tanpa adanya ekstraksi fitur pada data secara khusus. Sistem tersebut memiliki parameter atau bobot yang berfungsi sebagai ektraksi fitur maupun sebagai pengklasifikasi. Data yang diproses menghasilkan output yang diharapkan semirip mungkin dengan data output yang sesungguhnya. CNN merupakan arsitektur deep learning yang mampu mereduksi dimensi pada data tanpa menghilangkan ciri atau fitur pada data tersebut. Pada penelitian ini akan dikembangkan model hybrid CNN (Convolutional Neural Networks) untuk mengekstraksi fitur dan beberapa algoritma klasifikasi untuk mengidentifikasi spesies ikan. Algoritma klasifikasi yang digunakan pada penelitian ini adalah : Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbor (KNN), Random Forest, Backpropagation.

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Machine learning in the diagnosis of Myocardial Infarction with Non-Obstructive Coronary Arteries

European Heart Journal ◽

10.1093/eurheartj/ehab724.3067 ◽

2021 ◽

Vol 42 (Supplement_1) ◽

Author(s):

M J Espinosa Pascual ◽

P Vaquero Martinez ◽

V Vaquero Martinez ◽

J Lopez Pais ◽

B Izquierdo Coronel ◽

...

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Support Vector Machine ◽

Logistic Regression ◽

Random Forest ◽

Obstructive Coronary Artery Disease ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Classification Model ◽

Support Vector

Abstract Introduction Out of all patients admitted with Myocardial Infarction, 10 to 15% have Myocardial Infarction with Non-Obstructive Coronaries Arteries (MINOCA). Classification algorithms based on deep learning substantially exceed traditional diagnostic algorithms. Therefore, numerous machine learning models have been proposed as useful tools for the detection of various pathologies, but to date no study has proposed a diagnostic algorithm for MINOCA. Purpose The aim of this study was to estimate the diagnostic accuracy of several automated learning algorithms (Support-Vector Machine [SVM], Random Forest [RF] and Logistic Regression [LR]) to discriminate between people suffering from MINOCA from those with Myocardial Infarction with Obstructive Coronary Artery Disease (MICAD) at the time of admission and before performing a coronary angiography, whether invasive or not. Methods A Diagnostic Test Evaluation study was carried out applying the proposed algorithms to a database constituted by 553 consecutive patients admitted to our Hospital with Myocardial Infarction. According to the definitions of 2016 ESC Position Paper on MINOCA, patients were classified into two groups: MICAD and MINOCA. Out of the total 553 patients, 214 were discarded due to the lack of complete data. The set of machine learning algorithms was trained on 244 patients (training sample: 75%) and tested on 80 patients (test sample: 25%). A total of 64 variables were available for each patient, including demographic, clinical and laboratorial features before the angiographic procedure. Finally, the diagnostic precision of each architecture was taken. Results The most accurate classification model was the Random Forest algorithm (Specificity [Sp] 0.88, Sensitivity [Se] 0.57, Negative Predictive Value [NPV] 0.93, Area Under the Curve [AUC] 0.85 [CI 0.83–0.88]) followed by the standard Logistic Regression (Sp 0.76, Se 0.57, NPV 0.92 AUC 0.74 and Support-Vector Machine (Sp 0.84, Se 0.38, NPV 0.90, AUC 0.78) (see graph). The variables that contributed the most in order to discriminate a MINOCA from a MICAD were the traditional cardiovascular risk factors, biomarkers of myocardial injury, hemoglobin and gender. Results were similar when the 19 patients with Takotsubo syndrome were excluded from the analysis. Conclusion A prediction system for diagnosing MINOCA before performing coronary angiographies was developed using machine learning algorithms. Results show higher accuracy of diagnosing MINOCA than conventional statistical methods. This study supports the potential of machine learning algorithms in clinical cardiology. However, further studies are required in order to validate our results. FUNDunding Acknowledgement Type of funding sources: None. ROC curves of different algorithms

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Ovarian cancer diagnosis using pretrained mask CNN-based segmentation with VGG-19 architecture

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2021-0098 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Kavitha Senthil ◽

Vidyaathulasiramam

Keyword(s):

Neural Network ◽

Ovarian Cancer ◽

Support Vector Machine ◽

Logistic Regression ◽

Random Forest ◽

Cancer Diagnosis ◽

Support Vector ◽

Hybrid Neural Network ◽

Cancer Prediction ◽

The Neural Network

Abstract Objectives This paper proposed the neural network-based segmentation model using Pre-trained Mask Convolutional Neural Network (CNN) with VGG-19 architecture. Since ovarian is very tiny tissue, it needs to be segmented with higher accuracy from the annotated image of ovary images collected in dataset. This model is proposed to predict and suppress the illness early and to correctly diagnose it, helping the doctor save the patient's life. Methods The paper uses the neural network based segmentation using Pre-trained Mask CNN integrated with VGG-19 NN architecture for CNN to enhance the ovarian cancer prediction and diagnosis. Results Proposed segmentation using hybrid neural network of CNN will provide higher accuracy when compared with logistic regression, Gaussian naïve Bayes, and random Forest and Support Vector Machine (SVM) classifiers.

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Image Classification of Tourist Attractions with K-Nearest Neighbor, Logistic Regression, Random Forest, and Support Vector Machine

International Journal on Advanced Science Engineering and Information Technology ◽

10.18517/ijaseit.10.6.9098 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2207

Author(s):

Herry Sujaini

Keyword(s):

Support Vector Machine ◽

Logistic Regression ◽

Random Forest ◽

Image Classification ◽

Nearest Neighbor ◽

Support Vector ◽

K Nearest Neighbor ◽

Tourist Attractions

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Klasifikasi Jenis Pemeliharaan dan Perawatan Container Crane menggunakan Algoritma Machine Learning

MATICS ◽

10.18860/mat.v13i1.11525 ◽

2021 ◽

Vol 13 (1) ◽

pp. 21-27

Author(s):

Via Ardianto Nugroho ◽

Derry Pramono Adi ◽

Achmad Teguh Wibowo ◽

MY Teguh Sulistyono ◽

Agustinus Bimo Gumelar

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Logistic Regression ◽

Random Forest ◽

Decision Tree ◽

Nearest Neighbor ◽

Support Vector ◽

K Nearest Neighbor ◽

Container Crane ◽

Model Tree

Pada industri jasa pelayanan peti kemas, Terminal Nilam merupakan pelanggan dari PT. BIMA, yang secara khusus bergerak dibidang jasa perbaikan dan perawatan alat berat. Terminal ini menjadi sentral tempat untuk melakukan aktifitas bongkar muat peti kemas domestik yang memiliki empat buah container crane untuk melayani dua kapal. Proses perawatan alat berat seperti container crane yang selama ini beroperasi, agaknya kurang memperhatikan data pengelompokkan atau klasifikasi jenis perawatan yang dibutuhkan oleh alat berat tersebut. Di kemudian hari, alat berat dapat menunjukkan kinerja yang tidak maksimal bahkan dapat berujung pada kecelakaan kerja. Selain itu, kelalaian perawatan container crane juga dapat menyebabkan pembengkakan biaya perawatan lanjut. Target produksi bongkar muat dapat berkurang dan juga keterlambatan jadwal kapal sandar sangat mungkin terjadi. Metode pembelajaran menggunakan mesin atau biasa disebut dengan Machine Learning (ML), dengan mudah dapat melenyapkan kemungkinan-kemungkinan tersebut. ML dalam penelitian ini, kami rancang agar bekerja dengan mengidentifikasi lalu mengelompokkan jenis perawatan container crane yang sesuai, yaitu ringan atau berat. Metode ML yang pilih untuk digunakan dalam penelitian ini yaitu Random Forest, Support Vector Machine, k-Nearest Neighbor, Naïve Bayes, Logistic Regression, J48, dan Decision Tree. Penelitian ini menunjukkan keberhasilan ML model tree dalam melakukan pembelajaran jenis data perawatan container crane (numerik dan kategoris), dengan J48 menunjukkan performa terbaik dengan nilai akurasi dan nilai ROC-AUC mencapai 99,1%. Pertimbangan klasifikasi kami lakukan dengan mengacu kepada tanggal terakhir perawatan, hour meter, breakdown, shutdown, dan sparepart.

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