Incomplete data classification with voting based extreme learning machine

Extreme learning machine (ELM) was proposed as a new efficient learning algorithm for single-hidden layer feedforward neural networks (SLFN) in recent years. It is featured by its much faster training speed and better generalization performance over traditional SLFN learning techniques. However, ELM cannot deal directly with incomplete data which widely exists in real-world applications. In this paper, we propose a new algorithm to handle incomplete data with voting based extreme learning machine (V-ELMI). V-ELMI did not rely on any assumptions about missing values. It first obtains a group of data subsets according to the missing values of the training set. Then, it applies mutual information to measure the importance degree of each data subsets. After that, it trains a group of subclassifiers on these data subsets by applying ELM as base learning algorithm. Finally, for a given test sample with missing values, V-ELMI selects the subclassifiers whose input did not require the missing values to predict it. And final prediction is determined by weighted majority voting according to the mean value of the norms of the output weights and the importance degree of each available subclassifier. Experimental results on 15 UCI incomplete datasets and 5 UCI complete datasets have shown that, V-ELMI generally has better performance than the algorithms compared. Moreover, compared with the classification algorithms based on neural network ensemble (NNE), V-ELMI can greatly improve algorithm computational efficiency.