Keywords : Support Vector Machine

A Survey: Effective Machine Learning Based Classification Algorithm for Medical Dataset

Mahalakshmi G.; Shimaali Riyasudeen; Sairam R; Hari Sanjeevi R; Raghupathy B.

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue 9S, Pages 28-33
DOI: 10.47392/irjash.2021.245

Machine Learning is defined as nothing but as like humans learn from their experience likewise machine learns from experience. Here experience is nothing but the training and testing the machine with the. There are many techniques are available to train and test the system like Data Mining algorithms, Machine Learning Algorithms and Deep Learning Algorithms. It is not that all the algorithms will provide better results. Also there are many kinds of datasets is available. In this paper, the main focus is on Medical Dataset which requires more attention nowadays. And the algorithm we focus is on Machine Learning which contains different classification algorithms. Applying all the classification algorithms to the dataset and finding the best algorithm for the medical dataset with the highest accuracy. We have trained the dataset using seven classification algorithms called Naive Bayes, Random Forest, Support Vector Machine, Decision Tree, KNN, KSVM. After the implementation of each algorithm, we came up with a conclusion that Random Forest is the best algorithm for medical dataset which gives 100%accuracy among these seven Classification algorithms.

Performance Analysis of Feature Selection Techniques for Text Classification

Hemlata Patel; Dhanraj Verma

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue Special Issue ICSTM 12S, Pages 44-50
DOI: 10.47392/irjash.2020.259

Internet is a suitable, highly available and low cost publishing medium. Therefore a significant data is hosted and published using websites. In this domain some amount of data is directly present for common people and some of data is not publically distributed. Such kinds of data are utilizable by service providers and administrators for business intelligence and other similar applications. In this presented work the web data analysis or mining is the key area of investigation and experimental study. The web data mining can be dividing in three major classes i.e. web content mining, web structure mining and web usages mining. In this work the web content mining and web usages mining is taken into consideration. First of all the web content mining is explored thus a system is developed for making comparative performance study of different content feature selection techniques. In this experiment the GINI index, Information Gain, DFS and Odd Ratio is compared using a real world collection of web pages. In order to classify the extracted features from the web contents the SVM (Support Vector Machine) is applied. The comparative study demonstrates the IG and GI is the suitable feature selection techniques that work well with the SVM classifier.