Extreme Learning Machine (ELM) is popular in batch learning, sequential and progressive due to its speed, easy integration, generalization ability. While, Traditional ELM cannot train massive data rapidly efficiently memory residence, high time space complexity. In ELM, the hidden layer typically necessitates a huge number of nodes. Furthermore, there no certainty that arrangement weights biase...

Worldwide, there has been a rapid growth in interest in rough set theory and its applications in recent years. Evidence of this can be found in the increasing number of high-quality articles on rough sets and related topics that have been published in a variety of international journals, symposia, workshops, and international conferences in recent years. In addition, many international workshop...

This paper presents a machine learning approach to characterizing premonitory factors of earthquake. The characteristic asymmetric distribution of seismic events and sampling limitations make it difficult to apply the conventional statistical predictive techniques. The paper shows that inductive machine learning techniques such as rough set theory and decision tree (C4.5 algorithm) allows devel...