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Learning to Predict: INC2.5
January-February 1997 (vol. 9 no. 1)
pp. 168-173

Abstract—This paper discusses INC2.5, an incremental concept formation system. The goal of INC2.5 is to form a hierarchy of concept descriptions based on previously seen instances which will be used to predict the classification of a new instance description. Each subtree of the hierarchy consists of instances which are similar to each other. The further from the root, the greater the similarity is between the instances within the same groupings. The ability to classify instances based on their description has many applications. For example, in the medical field doctors are required daily to diagnose patients; in other words, classify patients according to their symptoms. INC2.5 has been successfully applied to several domains, including breast cancer, general trauma, congressional voting records, and the monk's problems.

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Index Terms:
Concept formation, diagnosis, database mining, knowledge acquisition, similarity-based learning.
Citation:
Mirsad Hadzikadic, Benjamin F. Bohren, "Learning to Predict: INC2.5," IEEE Transactions on Knowledge and Data Engineering, vol. 9, no. 1, pp. 168-173, Jan.-Feb. 1997, doi:10.1109/69.567059
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