Open AccessRegression Analysis of Solar Flares: A Multilayer Perceptron Approach with Feature Selection Techniques
Publication year - 2020
Publication title -
international journal of computers and communications
Language(s) - English
Resource type - Journals
ISSN - 2074-1294
DOI - 10.46300/91013.2020.14.14
Subject(s) - feature selection , computer science , artificial intelligence , multilayer perceptron , perceptron , machine learning , regression , regression analysis , linear regression , feature (linguistics) , variance (accounting) , selection (genetic algorithm) , cross validation , data mining , pattern recognition (psychology) , artificial neural network , statistics , mathematics , linguistics , philosophy , accounting , business
In this paper, we are going to analyze and test the solar flare dataset from the UCI Machine Learning Repository [10], by improving it using feature selection techniques such as stepwise regression, detecting the most effective attributes, importance ranker using k-fold and leave-one-out cross validation methods. We are going test the model by evaluating the dataset using Multi-linear regression, by looking at the P-values and the VIF to show the effectiveness of the dataset attributes. Multilayer perceptron model will be created using the holdout regression by partitioning the dataset into training and testing to model the testing dataset into an MLP model with 5 hidden layers. The model will show the mean absolute error and variance of the model to test its accuracy.