SMO Classifier Research Articles

Feature Selection and Classification Pairwise Combinations for High-dimensional Tumour Biomedical Datasets

This paper concerns classification of high-dimensional yet small sample size biomedical data and feature selection aimed at reducing dimensionality of the microarray data. The research presents a comparison of pairwise combinations of six classification strategies, including decision trees, logistic model trees, Bayes network, Na¨ive Bayes, k-nearest neighbours and sequential minimal optimization algorithm for training support vector machines, as well as seven attribute selection methods: Correlation-based Feature Selection, chi-squared, information gain, gain ratio, symmetrical uncertainty, ReliefF and SVM-RFE (Support Vector Machine-Recursive Feature Elimination). In this paper, SVMRFE feature selection technique combined with SMO classifier has demonstrated its potential ability to accurately and efficiently classify both binary and multiclass high-dimensional sets of tumour specimens.

Comparative Analysis of Text Categorizer on Science and Technology Intelligence

In order to more effectively classify the science and technology intelligence text, the idea that classifying science and technology intelligence text categorization based on different classifiers is proposed. The experiment is done with two thousand Chinese texts based on three different classifiers in this paper. Among these classifiers, the rate of correctly classified instances with NaiveBayes Classifier is 96.95 percent and J48 Classifiers is 97.59. The highest of three classifiers is SMO Classifier and its correct rate is 98.65 percent. According to the analysis of experimental results, it is proved that the idea proposed is applicable to science and technology intelligence text categorization and it is able to meet the needs of text categorization.

Multistage approach for clustering and classification of ECG data

Accurate and fast approaches for automatic ECG data classification are vital for clinical diagnosis of heart disease. To this end, we propose a novel multistage algorithm that combines various procedures for dimensionality reduction, consensus clustering of randomized samples and fast supervised classification algorithms for processing of the highly dimensional large ECG datasets. We carried out extensive experiments to study the effectiveness of the proposed multistage clustering and classification scheme using precision, recall and F-measure metrics. We evaluated the performance of numerous combinations of various methods for dimensionality reduction, consensus functions and classification algorithms incorporated in our multistage scheme. The results of the experiments demonstrate that the highest precision, recall and F-measure are achieved by the combination of the rank correlation coefficient for dimensionality reduction, HBGF consensus function and the SMO classifier with the polynomial kernel.

Performance Improvement with Combining Multiple Approaches to Diagnosis of Thyroid Cancer

There are a lot of diseases that carry death risk when these diseases are infected to human body, if early measures are not taken. Thyroid cancer is one of them. In USA, number of thyroid cancer cases resulted in death in only 2013 shows necessity of early fight with this disease. This study aims performance improvement in diagnosis of thyroid cancer with machine learning techniques. Study consists of 3 phases. In the first phase, BayesNet, NaiveBayes, SMO, Ibk and Random Forest classifiers have been trained with thyroid cancer train dataset. In the second phase, trained classifiers have been tested with thyroid cancer test dataset and the obtained performance results have been compared. In the third and last phase, approaches named above have been integrated to algorithm AdaboostMI to show difference between of ensemble classifiers from conventional individual classifiers and first two phases have been repeated. With using ensemble approaches performance improvement has been achieved in diagnosis of thyroid cancer. Also, kappa, accuracy and MCC values obtained from these classifier models have been explained in tables and effects on diagnosis of the disease have been shown with ROC graphics. All of these operations have been carried out with WEKA data mining program.

Off-line Recognition of Persian Handwritten Digits using Statistical Concepts

In this paper a novel method for recognition of Persian handwritten digits is proposed. This approach uses central moments, Covariance, Median and Variance that are obtained at each the box digit image as the features set. The features set consist of 140 dimensions for each instance. In the classification phase of our proposed method the support vector machines, K-Nearest Neighbor and Sequential Minimal Optimization separately are employed. In this paper is used the principal components analysis (PCA) to reduce dimension features set. the performance of these three classifiers is observed on this application in terms of the correct classification and misclassification and how the performance of K-Nearest Neighbor classifier can be improved by varying the value of k.To evaluate our proposed scheme a database of Persian handwritten digits consist of 1699 handwritten digit images is used. In the best case proposed scheme using SMO classifiers yields a recognition rate of 92.3875% for handwritten Farsi numerals.