TOSELM: Timeliness Online Sequential Extreme Learning Machine

For handling data and training model, existing machine learning methods do not take timeliness problem into consideration. Timeliness here means the data distribution or the data trend changes with time passing by. Based on timeliness management scheme, a novel machine learning algorithm Timeliness Online Sequential Extreme Learning Machine (TOSELM) is proposed, which improves Online Sequential Extreme Learning Machine (OSELM) with central tendency and dispersion characteristics of data to deal with timeliness problem. The performance of proposed algorithm has been validated on several simulated and realistic datasets, and experimental results show that TOSELM utilizing adaptive weight scheme and iteration scheme can achieve higher learning accuracy, faster convergence and better stability than other machine learning methods. (C) 2013 Elsevier B.V. All rights reserved.