Performance Of Support Vector Research Articles

Customer Churn Prediction Using Machine Learning Algorithms

Abstract: Customer churn is a serious problem in the telecommunications industry and occurs more often. Customer churn is the percentage of customers that stopped using your company's product or service during a certain time frame. One of the most important problems in predicting customer churn is the imbalanced data, which has been tried to be solved and compared with different methods. The machine learning algorithms will be use in this paper are Decision Tree, Support Vector Machine, Random Forest . Also, the performance of support vector were better than other algorithms.

Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing

Predicting ICU readmission risk will help physicians make decisions regarding discharge. We used discharge summaries to predict ICU 30-day readmission risk using text mining and machine learning (ML) with data from the Medical Information Mart for Intensive Care III (MIMIC-III). We used Natural Language Processing (NLP) and the Bag-of-Words approach on discharge summaries to build a Document-Term-Matrix with 3000 features. We compared the performance of support vector machines with the radial basis function kernel (SVM-RBF), adaptive boosting (AdaBoost), quadratic discriminant analysis (QDA), least absolute shrinkage and selection operator (LASSO), and Ridge Regression. A total of 4000 patients were used for model training and 6000 were used for validation. Using the bag-of-words determined by NLP, the area under the receiver operating characteristic (AUROC) curve was 0.71, 0.68, 0.65, 0.69, and 0.65 correspondingly for SVM-RBF, AdaBoost, QDA, LASSO, and Ridge Regression. We then used the SVM-RBF model for feature selection by incrementally adding features to the model from 1 to 3000 bag-of-words. Through this exhaustive search approach, only 825 features (words) were dominant. Using those selected features, we trained and validated all ML models. The AUROC curve was 0.74, 0.69, 0.67, 0.70, and 0.71 respectively for SVM-RBF, AdaBoost, QDA, LASSO, and Ridge Regression. Overall, this technique could predict ICU readmission relatively well.

Evaluating bank solvency with support vector machines

Banks as financial intermediaries play a very useful role in economic growth by facilitating the flow of funds to various sectors of the economy. Deterioration in a bank's performance and potential failure of the bank may lead to loss of confidence in the financial system that can result in loss of household savings and non-availability of funds to the business sector for economic expansion and growth. Banking regulators around the world are always looking for ways to identify sooner the banks that can be at risk of failure so that corrective action can be taken with minimal disruption to the economy. This study illustrates the use of support vector machines, an artificial intelligence technique, to predict the pending insolvency of a bank so that regulators can take appropriate steps to prevent a 'domino effect'. The study also compares the performance of support vector machines to multiple discriminant analysis in identifying 'unsafe' banks. To alleviate the problem of bias in the training set and to examine the robustness of support vector machine classifiers in identifying unsafe banks, we cross-validate our results through seven different samples of the data.

Prediction performance of support vector machines on input vector normalization methods

Support vector machines (SVM) based on the statistical learning theory is currently one of the most popular and efficient approaches for pattern recognition problem, because of their remarkable performance in terms of prediction accuracy. It is, however, required to choose a proper normalization method for input vectors in order to improve the system performance. Various normalization methods for SVMs have been studied in this research and the results showed that the normalization methods could affect the prediction performance. The results could be useful for determining a proper normalization method to achieve the best performance in SVMs.