A Review of Method of Stream data classification through Optimized Feature Evolution Process (International Journal of Engineering and Computer Science, 2014)
Dynamic changing nature of stream data are induced a difficulty of training pattern and process of class labeling in classification. The stream data classification has some difficulty such as feature evaluation, data drift, concept evaluation and infinite length. The infinite length and feature evaluation is more realistic problem in stream data classification technique. Different authors used different method such as data miner and tree based approach for reduced such types of issues. In this paper we discuss the stream data classification process and method of these classification techniques. Optimized feature evaluation process reduces the feature evaluation using ensemble technique. The feature evaluation process generates a new class label for classification. Some authors used optimization technique such as neural network, ANT colony optimization and genetic algorithm. The feature optimization technique improved the performance of stream data classification. Optimization technique for feature evaluation process also discussed in this paper.