Abstract
The internet is faster than the speed of light, memory storage and computing power has moved to the cloud. Big Data Analytics plays a vital role to segregate the data in some order. There is a huge amount of data available in the Information Industry. This data is of no use until it has converted into useful information. It is necessary to analyse this huge amount of data and extract useful information from it. An algorithm in data mining (or machine learning) is a set of heuristics and calculations that creates a model from data. To create a model, the algorithm first analyses the data you provide, looking for specific types of patterns or trends. The algorithm uses the results of this analysis over much iteration to find the optimal parameters for creating the mining model. These parameters have been applied across the entire data set to extract actionable patterns and detailed statistics. Included in this category is a very advanced technique and tool called “predictive algorithms”. Predictive algorithms have revolutionized the way we view the future of data and have demonstrated the big strides of computing technology. In this paper, we discussed about the criteria used to choose the right predictive model algorithm.
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References
Brameier, M., Banzhaf, W.: A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining. Fachbereich Informatik University at Dortmund 44221 Dortmund, Germany
Nguyen, T.T., Davis, D.N.: Predicting Cardio Vascular Risk Using Neural Net Techniques. University of Hull, Hull
Harold Robinson, Y., Golden Julie, E.: MTPKM: Multipart trust based public key management technique to reduce security vulnerability in mobile ad-hoc networks. Wirel. Pers. Commun. 109, 739–760 (2019)
Harold Robinson, Y., Santhana Krishnan, R., Golden Julie, E., Kumar, R., Son, L.H., Thong, P.H.: Neighbor knowledge-based rebroadcast algorithm for minimizing the routing overhead in mobile ad-hoc networks. Ad Hoc Netw. 93, 1–13 (2019)
Gao, D.W., Wang, P., Liang, H.: Optimization of hidden nodes and training times in ANN-QSAR model. College of Forest Resources and Environment, Northeast Forestry University, Harbin 150040, China, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, Chin
Davis, D.N., Nguyen, T.T.T.: Generating and verifying risk prediction models using data mining (A case study from cardiovascular medicine). Department of Computer Science, University of Hull, Cottingham Road, Hull, HU6 7RX, UK
Carneiro, E.M.: Cluster analysis and artificial neural networks: a case study in credit card fraud detection. In: 2015 IEEE International
Al-Jumeily, D.: Methods and Techniques to Support the Development of Fraud Detection System. IEEE (2015)
Mishra, M.K.: A comparative study of Chebyshev functional link artificial neural network, multi-layer perceptron and decision tree for credit card fraud detection. In: 2014 13th International Conference on Information Technology (2014)
Balaji, S., Golden Julie, E., Harold Robinson, Y.: Development of fuzzy based energy efficient cluster routing protocol to increase the lifetime of wireless sensor networks. Mobile Netw. Appl. 24(2), 394–406 (2019)
Mareeswari, V.: Prevention of credit card fraud detection based on HSVM. In: 2016 IEEE International Conference on Information Communication and Embedded System (2016)
Assis, C.A.S.: A genetic programming approach for fraud detection in electronic transactions. In: 2015 Second International Conference on Advances in Computing and Communication Engineering (ICACCE) (2015)
Harvey, D.Y.: Automated feature design for numeric sequence classification by genetic programming. IEEE Trans. Evol. Comput. 19(4) (2015)
Balaji, S., Golden Julie, E., Harold Robinson, Y., Kumar, R., Thong, P.H., Son, L.H.: Design of a security-aware routing scheme in mobile ad-hoc network using repeated game model. Comput. Stand. Inter. 66, (2019)
Sahin, Y., Bulkan, S., Duman, E.: A cost-sensitive decision tree approach for fraud detection. Expert Syst. Appl. 40, 5916–5923 (2013)
Harold Robinson, Y., Balaji, S., Golden Julie, E.: FPSOEE: Fuzzy-enabled particle swarm optimization-based energy-efficient algorithm in mobile ad-hoc networks. J. Intell. Fuzzy Syst. 36(4), 3541–3553 (2019)
Van Vlasselaer, V.: APATE: a novel approach for automated credit card transaction fraud detection using network-based extensions. Published in Decision Support Systems (2015)
Kültür, Y.: A novel cardholder behavior model for detecting credit card fraud. In: IEEE International Conference on Commuting and Communication Engineering (2015)
Harold Robinson, Y., Rajaram, M.: Energy-aware multipath routing scheme based on particle swarm optimization in mobile ad hoc networks. Sci. World J., 1–9 (2015)
Harold Robinson, Y., Rajaram, M.: A memory aided broadcast mechanism with fuzzy classification on a device-to-device mobile ad hoc network. 90(2), 769–791 (2016)
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Gopala Krishnan, C., Golden Julie, E., Harold Robinson, Y. (2020). Predictive Algorithm and Criteria to Perform Big Data Analytics. In: Balas, V., Solanki, V., Kumar, R. (eds) Internet of Things and Big Data Applications. Intelligent Systems Reference Library, vol 180. Springer, Cham. https://doi.org/10.1007/978-3-030-39119-5_16
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DOI: https://doi.org/10.1007/978-3-030-39119-5_16
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