• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1306-1325
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• 2019

As the world is moving towards digitization, data is generated from various sources at a faster rate. It is getting humungous and is termed as big data. The financial sector is one domain which needs to leverage the big data being generated to identify financial risks, fraudulent activities, and so on. The design of predictive models for such financial big data is imperative for maintaining the health of the country's economics. Financial data has many features such as transaction history, repayment data, purchase data, investment data, and so on. The main problem in predictive algorithm is finding the right subset of representative features from which the predictive model can be constructed for a particular task. This paper proposes a correlation-based method using submodular optimization for selecting the optimum number of features and thereby, reducing the dimensions of the data for faster and better prediction. The important proposition is that the optimal feature subset should contain features having high correlation with the class label, but should not correlate with each other in the subset. Experiments are conducted to understand the effect of the various subsets on different classification algorithms for loan data. The IBM Bluemix BigData platform is used for experimentation along with the Spark notebook. The results indicate that the proposed approach achieves considerable accuracy with optimal subsets in significantly less execution time. The algorithm is also compared with the existing feature selection and extraction algorithms.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.381-388
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• 2020

Weapon target assignment problem is an essential technology for automating the operator's rapid decision-making support in a battlefield situation. Weapon target assignment problem is a kind of the optimization problem that can build up an objective function by maximizing the number of threat target destructed or maximizing the survival rate of the protected assets. Weapon target assignment problem is known as the NP-Complete, and various studies have been conducted on it. Among them, a greedy heuristic algorithm which guarantees (1-1/e) approximation has been considered a very practical method in order to enhance the applicability of the real weapon system. In this paper, we formulated the weapon target assignment problem for supporting decision-making at the level of artillery. The lazy strategy based on hierarchical structure is proposed to accelerate the greedy algorithm. By experimental results, we show that our algorithm is more efficient in processing time and support the same level of the objective function value with the basic greedy algorithm.