scholarly journals A Clinical Decision Support Tool to Detect Invasive Ductal Carcinoma in Histopathological Images Using Support Vector Machines, Naïve-Bayes, and K-Nearest Neighbor Classifiers

2020 ◽  
Author(s):  
Kyra Mikaela M. Lopez ◽  
Ma. Sheila A. Magboo
Keyword(s):  
Support Vector Machines ◽  
Invasive Ductal Carcinoma ◽  
Naive Bayes ◽  
Ductal Carcinoma ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Support Tool ◽  
Vector Machines

This study aims to describe a model that will apply image processing and traditional machine learning techniques specifically Support Vector Machines, Naïve-Bayes, and k-Nearest Neighbors to identify whether or not a given breast histopathological image has Invasive Ductal Carcinoma (IDC). The dataset consisted of 54,811 breast cancer image patches of size 50px x 50px, consisting of 39,148 IDC negative and 15,663 IDC positive. Feature extraction was accomplished using Oriented FAST and Rotated BRIEF (ORB) descriptors. Feature scaling was performed using Min-Max Normalization while K-Means Clustering on the ORB descriptors was used to generate the visual codebook. Automatic hyperparameter tuning using Grid Search Cross Validation was implemented although it can also accept user supplied hyperparameter values for SVM, Naïve Bayes, and K-NN models should the user want to do experimentation. Aside from computing for accuracy, the AUPRC and MCC metrics were used to address the dataset imbalance. The results showed that SVM has the best overall performance, obtaining accuracy = 0.7490, AUPRC = 0.5536, and MCC = 0.2924.

scholarly journals An Enhanced Corpus for Arabic Newspapers Comments

2020 ◽  
Vol 17 (5) ◽  
pp. 789-798
Author(s):  
Hichem Rahab ◽  
Abdelhafid Zitouni ◽  
Mahieddine Djoudi
Keyword(s):  
Support Vector Machines ◽  
Web Sites ◽  
Naive Bayes ◽  
Nearest Neighbors ◽  
Naïve Bayes ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Vector Machines

In this paper, we propose our enhanced approach to create a dedicated corpus for Algerian Arabic newspapers comments. The developed approach has to enhance an existing approach by the enrichment of the available corpus and the inclusion of the annotation step by following the Model Annotate Train Test Evaluate Revise (MATTER) approach. A corpus is created by collecting comments from web sites of three well know Algerian newspapers. Three classifiers, support vector machines, naïve Bayes, and k-nearest neighbors, were used for classification of comments into positive and negative classes. To identify the influence of the stemming in the obtained results, the classification was tested with and without stemming. Obtained results show that stemming does not enhance considerably the classification due to the nature of Algerian comments tied to Algerian Arabic Dialect. The promising results constitute a motivation for us to improve our approach especially in dealing with non Arabic sentences, especially Dialectal and French ones

scholarly journals Mobile Money Fraud Prediction—A Cross-Case Analysis on the Efficiency of Support Vector Machines, Gradient Boosted Decision Trees, and Naïve Bayes Algorithms

Information ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 383
Author(s):  
Francis Effirim Botchey ◽  
Zhen Qin ◽  
Kwesi Hughes-Lartey
Keyword(s):  
Developing Countries ◽  
Support Vector Machines ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Mobile Money ◽  
Vector Machines ◽  
Boosted Decision Tree

The onset of COVID-19 has re-emphasized the importance of FinTech especially in developing countries as the major powers of the world are already enjoying the advantages that come with the adoption of FinTech. Handling of physical cash has been established as a means of transmitting the novel corona virus. Again, research has established that, been unbanked raises the potential of sinking one into abject poverty. Over the years, developing countries have been piloting the various forms of FinTech, but the very one that has come to stay is the Mobile Money Transactions (MMT). As mobile money transactions attempt to gain a foothold, it faces several problems, the most important of them is mobile money fraud. This paper seeks to provide a solution to this problem by looking at machine learning algorithms based on support vector machines (kernel-based), gradient boosted decision tree (tree-based) and Naïve Bayes (probabilistic based) algorithms, taking into consideration the imbalanced nature of the dataset. Our experiments showed that the use of gradient boosted decision tree holds a great potential in combating the problem of mobile money fraud as it was able to produce near perfect results.

A Semantic Scattering model for the automatic interpretation of English genitives

2009 ◽  
Vol 15 (2) ◽  
pp. 215-239 ◽  
Author(s):  
ADRIANA BADULESCU ◽  
DAN MOLDOVAN
Keyword(s):  
Support Vector Machines ◽  
Decision Trees ◽  
Naive Bayes ◽  
Word Sense Disambiguation ◽  
Naïve Bayes ◽  
Semantic Relations ◽  
Support Vector ◽  
Word Sense ◽  
Vector Machines ◽  
Bayes Algorithm

AbstractAn important problem in knowledge discovery from text is the automatic extraction of semantic relations. This paper addresses the automatic classification of thesemantic relationsexpressed by English genitives. A learning model is introduced based on the statistical analysis of the distribution of genitives' semantic relations in a corpus. The semantic and contextual features of the genitive's noun phrase constituents play a key role in the identification of the semantic relation. The algorithm was trained and tested on a corpus of approximately 20,000 sentences and achieved an f-measure of 79.80 per cent for of-genitives, far better than the 40.60 per cent obtained using a Decision Trees algorithm, the 50.55 per cent obtained using a Naive Bayes algorithm, or the 72.13 per cent obtained using a Support Vector Machines algorithm on the same corpus using the same features. The results were similar for s-genitives: 78.45 per cent using Semantic Scattering, 47.00 per cent using Decision Trees, 43.70 per cent using Naive Bayes, and 70.32 per cent using a Support Vector Machines algorithm. The results demonstrate the importance of word sense disambiguation and semantic generalization/specialization for this task. They also demonstrate that different patterns (in our case the two types of genitive constructions) encode different semantic information and should be treated differently in the sense that different models should be built for different patterns.

scholarly journals Sentiment Analysis of Student’s Opinion on Programming Assessment: Evaluation of Naïve Bayes over Support Vector Machines

2020 ◽  
Vol 10 (2) ◽  
Author(s):  
Mahmood Umar ◽  
Nor Bahiah Ahmad ◽  
Anazida Zainal
Keyword(s):  
Support Vector Machines ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Experimental Result ◽  
Support Vector ◽  
Small Data ◽  
Data Set ◽  
Vector Machines

This study investigates the performance of machine learning algorithms for sentiment analysis of students’ opinions on programming assessment. Previous researches show that Support Vector Machines (SVM) performs the best among all techniques, followed by Naïve Bayes (NB) in sentiment analysis. This study proposes a framework for classifying sentiments, as positive or negative using NB algorithm and Lexicon-based approach on small data set. The performance of NB algorithm was evaluated using SVM. NB and SVM conquer the Lexicon-based approach opinion lexicon technique in terms of accuracy in the specific area for which it is trained. The Lexicon-based technique, on the other hand, avoids difficult steps needed to train the classifier. Data was analyzed from 75 first year undergraduate students in School of Computing, Universiti Teknologi Malaysia taking programming subject. The student’s sentiments were gathered based on their opinions for the zero-score policy for unsuccessful compilation of program during skill-based test. The result of the study reveals that the students tend to have negative sentiments on programming assessment as it gives them scary emotions. The experimental result of applying NB algorithm yields a prediction accuracy of 85% which outperform both the SVM with 70% and Lexicon-based approach with 60% accuracy. The result shows that NB works better than SVM and Lexicon-based approach on small dataset. 

scholarly journals Prediction of Heart Disease using Machine Learning

2019 ◽  
Vol 8 (2S10) ◽  
pp. 474-477
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Support Vector Machines ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Data Set ◽  
Vector Machines ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification

Machine learning is one of the fast growing aspect in current world. Machine learning (ML) and Artificial Neural Network (ANN) are helpful in detection and diagnosis of various heart diseases. Naïve Bayes Classification is a vital approach of classification in machine learning. The heart disease consists of set of range disorders affecting the heart. It includes blood vessel problems such as irregular heart beat issues, weak heart muscles, congenital heart defects, cardio vascular disease and coronary artery disease. Coronary heart disorder is a familiar type of heart disease. It reduces the blood flow to the heart leading to a heart attack. In this paper the UCI machine learning repository data set consisting of patients suffering from heart disease is analyzed using Naïve Bayes classification and support vector machines. The classification accuracy of the patients suffering from heart disease is predicted using Naïve Bayes classification and support vector machines. Implementation is done using R language.

Detection of Loss Zones while Drilling Using Different Machine Learning Techniques

2021 ◽  
pp. 1-29
Author(s):  
Ahmed Alsaihati ◽  
Mahmoud Abughaban ◽  
Salaheldin Elkatatny ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Random Forests ◽  
Nearest Neighbors ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Learning Techniques ◽  
Vector Machines ◽  
Testing Set

Abstract Fluid loss into formations is a common operational issue that is frequently encountered when drilling across naturally or induced fractured formations. This could pose significant operational risks, such as well-control, stuck pipe, and wellbore instability, which, in turn, lead to an increase of well time and cost. This research aims to use and evaluate different machine learning techniques, namely: support vector machines, random forests, and K-nearest neighbors in detecting loss circulation occurrences while drilling using solely drilling surface parameters. Actual field data of seven wells, which had suffered partial or severe loss circulation, were used to build predictive models, while Well-8 was used to compare the performance of the developed models. Different performance metrics were used to evaluate the performance of the developed models. Recall, precision, and F1-score measures were used to evaluate the ability of the developed model to detect loss circulation occurrences. The results showed the K-nearest neighbors classifier achieved a high F1-score of 0.912 in detecting loss circulation occurrence in the testing set, while the random forests was the second-best classifier with almost the same F1-score of 0.910. The support vector machines achieved an F1-score of 0.83 in predicting the loss circulation occurrence in the testing set. The K-nearest neighbors outperformed other models in detecting the loss circulation occurrences in Well-8 with an F1-score of 0.80. The main contribution of this research as compared to previous studies is that it identifies losses events based on real-time measurements of the active pit volume.

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