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In recent years, the quantity of digital data being generated has increased considerably and is overwhelming the storage capacity. To overcome this problem, acquiring more and larger data storage is the simplest solution. But this solution is rather costly and may produce poisonous electronic garbage. A new fast and memory-efficient algorithm for learning and classifying these data without increasing the space and time complexities more than those of current learning and classifying algorithms is desirable. Although many one-pass online or incremental learning algorithms based on hyperellipsoidal functions for streaming data without retaining any learned data in fixed storage have been successfully developed for training streaming data, achieving high accuracy of any testing dataset is unstable and uncontrollable, depending on the experimental datasets. This paper proposes an improvement to these one-pass and fixed-storage learning algorithms so that the high accuracy of testing data can be significantly improved and stabilized, regardless of the experimental datasets. The concept is based on animal recoil behavior, which occurs when an animal moves away suddenly from something it dislikes. The behavior is mathematically modeled in forms of shrinking and shifting the hyperellipsoidal function during the training period to improve testing accuracy. The experimental results on 15 datasets improved the accuracy up to 8.16 % and also provided the highest or near-highest accuracy results in 10 datasets when compared to other algorithms.

Neural Learning With Recoil Behavior in Hyperellipsoidal Structure