scholarly journals Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 186
Author(s):  
Sami Bourouis ◽  
Yogesh Pawar ◽  
Nizar Bouguila
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Model Selection ◽  
Prior Information ◽  
Dynamic Textures ◽  
Generalization Capability ◽  
Gamma Mixtures ◽  
Variational Framework ◽  
Vector Clustering ◽  
Efficient Learning

Finite Gamma mixture models have proved to be flexible and can take prior information into account to improve generalization capability, which make them interesting for several machine learning and data mining applications. In this study, an efficient Gamma mixture model-based approach for proportional vector clustering is proposed. In particular, a sophisticated entropy-based variational algorithm is developed to learn the model and optimize its complexity simultaneously. Moreover, a component-splitting principle is investigated, here, to handle the problem of model selection and to prevent over-fitting, which is an added advantage, as it is done within the variational framework. The performance and merits of the proposed framework are evaluated on multiple, real-challenging applications including dynamic textures clustering, objects categorization and human gesture recognition.

scholarly journals Detection of Anxiety Expression From EEG Analysis Using Support Vector Machine

2019 ◽  
Vol 17 (1) ◽  
pp. 87-94
Author(s):  
Kou Yamada ◽  
Wan Junaidee bin Wan Hamat ◽  
Harris Majdi bin Ishak ◽  
Kotaro Hashikura ◽  
Takaaki Suzuki
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Learning Communities ◽  
Ranking Function ◽  
Support Vector ◽  
Eeg Analysis ◽  
Nonlinear Functions ◽  
Kernel Trick ◽  
Efficient Learning

Support Vector Machine (SVMs) have been extensively researched in data mining and machine learning communities for the last decade and actively applied to application in various domains. SVMs are typically used for learning classification, regression and ranking function. Two specials properties of SVMs are that SVMs achieve high generalization by maximizing the margin and support an efficient learning of nonlinear functions by kernel trick. In this paper, we present how to clarify when we feel anxiety by using SVM technique to estimate the condition of user.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Classification of Operational and Financial Variables Affecting the Bullwhip Effect in Indian Sectors: A Machine Learning Approach

2019 ◽  
Vol 12 (3) ◽  
pp. 171-179 ◽  
Author(s):  
Sachin Gupta ◽  
Anurag Saxena
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Product Life Cycle ◽  
Consumer Preference ◽  
Bullwhip Effect ◽  
Machine Learning Techniques ◽  
Chain Management ◽  
Financial Variables

Background: The increased variability in production or procurement with respect to less increase of variability in demand or sales is considered as bullwhip effect. Bullwhip effect is considered as an encumbrance in optimization of supply chain as it causes inadequacy in the supply chain. Various operations and supply chain management consultants, managers and researchers are doing a rigorous study to find the causes behind the dynamic nature of the supply chain management and have listed shorter product life cycle, change in technology, change in consumer preference and era of globalization, to name a few. Most of the literature that explored bullwhip effect is found to be based on simulations and mathematical models. Exploring bullwhip effect using machine learning is the novel approach of the present study. Methods: Present study explores the operational and financial variables affecting the bullwhip effect on the basis of secondary data. Data mining and machine learning techniques are used to explore the variables affecting bullwhip effect in Indian sectors. Rapid Miner tool has been used for data mining and 10-fold cross validation has been performed. Weka Alternating Decision Tree (w-ADT) has been built for decision makers to mitigate bullwhip effect after the classification. Results: Out of the 19 selected variables affecting bullwhip effect 7 variables have been selected which have highest accuracy level with minimum deviation. Conclusion: Classification technique using machine learning provides an effective tool and techniques to explore bullwhip effect in supply chain management.

scholarly journals Data mining techniques with machine learning algorithm to predict patients of heart disease

2021 ◽  
Vol 1088 (1) ◽  
pp. 012035
Author(s):  
Mulyawan ◽  
Agus Bahtiar ◽  
Githera Dwilestari ◽  
Fadhil Muhammad Basysyar ◽  
Nana Suarna
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Mining Techniques

Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

2021 ◽  
pp. 097215092098485
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Meta Analysis ◽  
Financial Statement ◽  
Research Articles ◽  
Financial Statement Fraud ◽  
Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

Algorithms and software for data mining and machine learning: a critical comparative view from a systematic review of the literature

2021 ◽  
Author(s):  
Gilda Taranto-Vera ◽  
Purificación Galindo-Villardón ◽  
Javier Merchán-Sánchez-Jara ◽  
Julio Salazar-Pozo ◽  
Alex Moreno-Salazar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Data Mining ◽  
Review Of The Literature
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