An Improved Learning Strategy to Classify Complex and High Dimensional Datasets
Most of the existing classification techniques concentrate on learning the datasets as a single similar unit, in spite of so many differentiating attributes and complexities involved. However, traditional classification techniques, require to analysis the dataset prior to learning and for not doing so they loss their performance in terms of accuracy and AUC. To this end, many of the machine learning problems can be very easily solved just by careful observing human learning and training nature and then mimic the same in the machine learning. To solve this dilemma, the authors propose a novel, simple and effective machine learning paradigm that explicitly exploits this important Similar-To-Different (S2D) human learning strategy, and implement it based on C4.5 efficiently.