Enhancement in Performance of Financial Crisis Prediction using Hybridization of Machine Learning Classifiers

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.

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Machine-Learning-Based External Plagiarism Detecting Methodology From Monolingual Documents

Feature Dimension Reduction for Content-Based Image Identification - Advances in Multimedia and Interactive Technologies ◽

10.4018/978-1-5225-5775-3.ch007 ◽

2018 ◽

pp. 122-139

Author(s):

Saugata Bose ◽

Ritambhra Korpal

Keyword(s):

Machine Learning ◽

Language Processing ◽

Confusion Matrix ◽

False Negative ◽

False Negative Rate ◽

Search Space ◽

Machine Learning Algorithms ◽

C4.5 Decision Tree ◽

N Gram ◽

Four Levels

In this chapter, an initiative is proposed where natural language processing (NLP) techniques and supervised machine learning algorithms have been combined to detect external plagiarism. The major emphasis is on to construct a framework to detect plagiarism from monolingual texts by implementing n-gram frequency comparison approach. The framework is based on 120 characteristics which have been extracted during pre-processing steps using simple NLP approach. Afterward, filter metrics has been applied to select most relevant features and supervised classification learning algorithm has been used later to classify the documents in four levels of plagiarism. Then, confusion matrix was built to estimate the false positives and false negatives. Finally, the authors have shown C4.5 decision tree-based classifier's suitability on calculating accuracy over naive Bayes. The framework achieved 89% accuracy with low false positive and false negative rate and it shows higher precision and recall value comparing to passage similarities method, sentence similarity method, and search space reduction method.

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An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers

Applied Sciences ◽

10.3390/app9112375 ◽

2019 ◽

Vol 9 (11) ◽

pp. 2375 ◽

Cited By ~ 7

Author(s):

Riaz Ullah Khan ◽

Xiaosong Zhang ◽

Rajesh Kumar ◽

Abubakar Sharif ◽

Noorbakhsh Amiri Golilarz ◽

...

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Network Traffic ◽

Traffic Classification ◽

Decision Tree Classifier ◽

Machine Learning Classifiers ◽

Learning Classifiers ◽

Average Accuracy ◽

Final Layer ◽

Tree Classifier

In recent years, the botnets have been the most common threats to network security since it exploits multiple malicious codes like a worm, Trojans, Rootkit, etc. The botnets have been used to carry phishing links, to perform attacks and provide malicious services on the internet. It is challenging to identify Peer-to-peer (P2P) botnets as compared to Internet Relay Chat (IRC), Hypertext Transfer Protocol (HTTP) and other types of botnets because P2P traffic has typical features of the centralization and distribution. To resolve the issues of P2P botnet identification, we propose an effective multi-layer traffic classification method by applying machine learning classifiers on features of network traffic. Our work presents a framework based on decision trees which effectively detects P2P botnets. A decision tree algorithm is applied for feature selection to extract the most relevant features and ignore the irrelevant features. At the first layer, we filter non-P2P packets to reduce the amount of network traffic through well-known ports, Domain Name System (DNS). query, and flow counting. The second layer further characterized the captured network traffic into non-P2P and P2P. At the third layer of our model, we reduced the features which may marginally affect the classification. At the final layer, we successfully detected P2P botnets using decision tree Classifier by extracting network communication features. Furthermore, our experimental evaluations show the significance of the proposed method in P2P botnets detection and demonstrate an average accuracy of 98.7%.

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Machine Learning Classifiers based on Predicting Membrane Protein using Decision Tree and Random Forest

2020 Fourth International Conference on Inventive Systems and Control (ICISC) ◽

10.1109/icisc47916.2020.9171220 ◽

2020 ◽

Author(s):

Anjna Jayant Deen ◽

Manasi Gyanchandani

Keyword(s):

Machine Learning ◽

Random Forest ◽

Membrane Protein ◽

Decision Tree ◽

Machine Learning Classifiers ◽

Learning Classifiers

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Machine Learning Based Classification Models for Financial Crisis Prediction

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d8362.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 4887-4893

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Financial Crisis ◽

Large Scale ◽

False Positive Rate ◽

False Negative ◽

False Negative Rate ◽

Small Scale ◽

Early Prediction ◽

Rbf Network

Financial Crisis Prediction (FCP) being the most complicated and expected problem to be solved from the context of corporate organization, small scale to large scale industries, investors, bank organizations and government agencies, it is important to design a framework to determine a methodology that will reveal a solution for early prediction of the Financial Crisis Prediction (FCP). Earlier methods are reviewed through the various works in statistical techniques applied to solve the problem. However, it is not sufficient to predict the results with much more intelligence and automated manner. The major objective of this paper is to enhance the early prediction of Financial Crisis in any organization based on machine learning models like Multilayer Perceptron, Radial basis Function (RBF) Network, Logistic regression and Deep Learning methods and conduct a comparative analysis of them to determine the best methods for Financial Crisis Prediction (FDP). The testing is conducted with globalized benchmark datasets namely German dataset, Weislaw dataset and Polish Dataset. The testing is performed in both WEKA and Rapid Miner Framework design and obtained with accuracies and other performance measures like False Positive Rate (FPR), False Negative Rate (FNR), Precision, Recall, F-score and Kappa that would determine the best result from specific algorithm that will intelligently identify the financial crisis before it actually occurs in an organization. The results achieved the algorithms DL, MLP, LR and RBF Network with accuracies 96%, 72.10%, 75.20% and 74% on German Dataset, 91.25%, 85.83%, 83.75% and 73.75% on Weislaw dataset, 99.70%, 96.30%, 96.21% and 96.14 on Polish dataset respectively. It is evident from all the predictive results and the analytics in Rapid Miner that Deep Learning (DL) is the best classifier and performer among other machine learners and classifiers. This method will enhance the future predictions and would provide efficient solutions for financial crisis predictions.

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