MCAIM: Modified CAIM Discretization Algorithm for Classification
نویسنده
چکیده
Discretization is a process of dividing a continuous attribute into a finite set of intervals to generate an attribute with small number of distinct values, by associating discrete numerical value with each of the generated intervals. Discretization is usually performed prior to the learning process and has played an important role in data mining and knowledge discovery. The results of CAIM are not satisfactory in some cases, led us to modify the algorithm. The Modified CAIM (MCAIM) results are compared with other discretization techniques for classification accuracy and generated the outperforming results. The intervals generated by MCAIM discretization are more in numbers, so to reduce them, the CAIR criterion is used to merge the intervals in MCAIM discretization. It gives better classification accuracy and the reduced number of intervals.
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