Optimal Feature Selection in Stream Data Classification Using Improved Ensemble Classifier for High Dimension Data - Pandey, Gaurav; Mishra, Nitin (International Journal of Computer Sciences and Engineering, 2025)
Dynamic feature evaluation and concept evaluation is major challenging task in the field of stream data classification. The continuity of data induced a new feature during classification process, but the classification process is predefined task for assigning data into class. Stream data comes into multiple feature sub-set format into infinite length. The infinite length not decided the how many class are assigned. Genetic algorithm is well known population based method. The performance of genetic algorithm is better than other optimization technique such as POS and ANT colony optimization. The dynamic nature of genetic algorithm maintains the dynamic feature evaluation. The optimization process goes through multiple stages in terms of selection of feature and optimization of feature. The optimized feature reduces the unclassified region of class during classification. The proposed method for stream data classification is MMCM-GA is implemented in MATLAB 7.8.0. And test the validation process used some reputed data set from UCI machine learning prosperity. These data are corpus, forest and finally used glass dataset. Our empirical evaluation of result shows better feature evaluation and minimization of error rate in comprehension of MCM stream data classification