Learning on High Frequency Stock Market Data Using Misclassified Instances in Ensemble

Learning on non-stationary distribution has been shown to be a very challenging problem in machine learning and data mining, because the joint probability distribution between the data and classes changes over time. Many real time problems suffer concept drift as they changes with time. For example, in stock market, the customer’s behavior may change depending on the season of the year and on the inflation. Concept drift can occurs in the stock market for a number of reasons for example, trader’s preference for stocks change over time, increases in a stock’s value may be followed by decreases. The objective of this paper is to develop an ensemble based classification algorithm for non-stationary data stream which would consider misclassified instances during learning process. In addition, we are presenting here an exhaustive comparison of proposed algorithms with state-of-the-art classification approaches using different evaluation measures like recall, f-measure and g-mean.