Open AccessEnhancement in Performance of Financial Crisis Prediction using Hybridization of Machine Learning Classifiers
Author(s) -
S. Anand Christy*,
Dr.R. Arunkumar
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.c8722.019320
Subject(s) - machine learning , artificial intelligence , confusion matrix , decision tree , computer science , artificial neural network , naive bayes classifier , gradient boosting , boosting (machine learning) , finance , multilayer perceptron , classifier (uml) , perceptron , support vector machine , random forest , economics
Financial Crisis has been the stern problem experienced by various organizations or even common people when interested in investing in any Financial institutions like banks, Funds development institutions etc. Hence it is mandatory that a reliable prediction system should be applied in early prediction of Financial Crisis Prediction thereby preventing investment in weak financial institutions that might lead to bankruptcy. The Paper focuses on designing a Hybrid Optimized Algorithm called Hybrid Unified Machine Classifier (HUMC) based on Machine Learning Technique that would be capable of identifying categorized and continuous variables in a financial crisis dataset and determine the confusion matrix that can be instilled in performance analysis tool comprising of analytics and prediction related to Accuracy, F-Score, Sensitivity, Specificity, False Positive Rate (FPR) and False Negative Rate (FNR) respectively. Early testing with the training set of Australian credit dataset were tested with machine learning classifiers like Decision Tree, PART, Naive Bayesian, RBF Network and Multilayer Perceptron algorithms with accuracies 85.50%, 83.62%, 77.24%, 82.75% and 84.93% respectively. The Algorithm HUMC was developed based on combining classification features from decision tree, identifying hidden nodes and model with boosting technique that could enhance the performance levels of the Financial Crisis Prediction. The design of algorithm comprised of best characteristics of both classification and neural networks that are capable to find categorization criteria in the dataset at the first level and also to find the hidden continuous data during the second stage respectively. The design of HUMC was implemented and tested with MATLAB. The Result showed that HUMC algorithm showed greater accuracy (86.25%) in comparison to other classifier models along with other performance measures. Thus, this algorithm enhances the prediction of Financial Crisis predictions with good performance.