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Driven by the emerging network data exchange and storage, XML documents classification has become increasingly important. Most existing representation model and conventional learning algorithm are defined on certain XML documents. However, in many real-world applications, XML datasets contain inherent uncertainty, which brings greater challenges to classification problem. In this paper, we propose a novel solution to classify uncertain XML documents, including uncertain XML documents representation and two uncertain learning algorithms based on Extreme Learning Machine. Experimental results show that our approaches exhibit prominent performance for uncertain XML documents classification problem. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the problem of extreme learning machine (ELM)-based microarray data classification. Different from the traditional classification problem, the goal in this case is not just to predict the class labels for the unseen samples, but to make clear what lead to the results, i.e., the genes involving with a specific disease. This is especially significant for biologists, since they need to decipher the causes of disease. As a black-box method, ELM could not measure up to the task by itself. In this work, we propose a diversified sequence feature selection-based framework to address the problem. In this framework, (1) a sequence model, EWave, is introduced to ensure the structural ordering information among genes exploitable; (2) a concept of irreducible sequence is proposed, where the genes work as an orderly whole to keep high confidence with a specific class and any reduction in the genes decreases the confidence much. An efficient sequence mining algorithm together with some effective pruning rules is developed to mine such sequences; and (3) we study how to extract a set of diversified sequence features as the representative of all mined results. The problem is proved to be NP-hard. A greedy algorithm is presented to approximate the optimal solution. Experimental results show that the proposed approach significantly improves the efficiency and the effectiveness of ELM w.r.t some widely used feature selection techniques. [ABSTRACT FROM AUTHOR]

Abstract: Extreme Learning Machine (ELM) as an emergent technology has shown its promising performance in many applications. This paper proposes a parallelized ELM ensemble based on the Min–Max Modular network (M3-network) to meet the challenge of the so-called big data. The proposed M3-ELM first decomposes classification problems into smaller subproblems, then trains an ELM for each subproblem, and in the end ensembles these ELMs with the M3-network. Twelve data sets including both benchmarks and real-world applications are employed to test the proposed method. The experimental results show that M3-ELM not only speeds up the training phrases by 1.6–4.6 times but also reduces the test errors by 0.37–19.51% compared with the normal ELM. The results also indicate that M3-ELM possesses scalability on large-scale tasks and accuracy improvement on imbalanced tasks. [Copyright &y& Elsevier]