A fast learning method for streaming and randomly ordered multi-class data chunks by using one-pass-throw-away class-wise learning concept

• We propose the 1-pass-throw-away learning method to classify a large or stream data.
• Parameter update for versatile elliptic function in chunk data case is presented.
• Both incremental and batch methods are used to compare with the proposed method.
• The method gives high accuracy in classification on 5-fold cross-validation test.
• The proposed method takes the fast learning time and less complexity on large data.

Presently, the amount of data occurring in several business and academic areas such as ATM transactions, web searches, and sensor data are tremendously and continuously increased. Classifying as well as recognizing patterns among these data in a limited memory space complexity are very challenging. Various incremental learning methods have proposed to achieve highly accurate results but both already learned data and new incoming data must be retained throughout the learning process, causing high space and time complexities. In this paper, a new neural learning method based on radial-shaped function and discard-after-learn concept in the data streaming environment was proposed to reduce the space and time complexities. The experimental results showed that the proposed method used 1 to 95 times fewer neurons and 1.2 to 2,700 times faster than the results produced by MLP, RBF, SVM, VEBF, ILVQ, ASC, and other incremental learning methods. It is also robust to the incoming order of data chunks.