Abstract
Finance is the elixir that builds the economy of the world and which has a direct impact in the development and advancement of societies. In the finance domain, it is critical to analyse the data as there are heavy risks involved for industries, governments, and even individuals. Any wrong or untimely decision may amount to huge losses and significantly impact businesses and lives. Whereas, better analysis results in mitigating these risks and help to make better decisions which in turn may help to increase profits abundantly. Machine learning is proving to be very useful to draw insights and make predictions in this domain due the availability and nature of financial data. It is finding its applications in investment banking, algorithmic trading, fraud detection, stock market forecasts, etc. This paper attempts to demonstrate an approach to improve the usefulness of machine learning techniques for classification and prediction in the domain of finance. The approach involves the use of genetic algorithms to improve the accuracy and efficiency of traditional algorithms and achieve optimization.
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Kanamarlapudi, A., Deshpande, K., Sharma, C. (2023). Classification and Prediction of Financial Datasets Using Genetic Algorithms. In: Shukla, A., Murthy, B.K., Hasteer, N., Van Belle, JP. (eds) Computational Intelligence. Lecture Notes in Electrical Engineering, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-19-7346-8_25
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DOI: https://doi.org/10.1007/978-981-19-7346-8_25
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