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Solving multi-instance problems with classifier ensemble based on constructive clustering
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A Multi-Instance Learning Algorithm Based on Normalized Radial Basis Function Network
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IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A comparison of classification accuracy of four genetic programming-evolved intelligent structures
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This paper introduces a new Grammar-Guided Genetic Programming algorithm for solving multi-instance Learning problems. This algorithm, called G3P-MI, is evaluated and compared with other Multi-Instance classification techniques on different application domains. Computational experiments show that the G3P-MI often obtains consistently better results than other algorithms in terms of accuracy, sensitivity and specificity. Moreover, it adds comprehensibility and clarity into the knowledge discovery process, expressing the information in the form of IF-THEN rules. Our results confirm that evolutionary algorithms are appropriate for dealing with multi-instance learning problems.