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In this era of big data, stream data classification which is one of typical data stream applications has become more and more significant and challengeable. In these applications, it is obvious that data classification is much more frequent than model training. The ratio of stream data to be classified is rapid and time-varying, so it is an important problem to classify the stream data efficiently with high throughput. In this paper, we first analyze and categorize the current data stream machine learning algorithms according to their data structures. Then, we propose stream data classification topology (SDC-Topology) on Storm. For the classification algorithms based on the matrix, we propose self-adaptive stream data classification framework (SASDC-Framework) for efficient stream data classification on Storm. In SASDC-Framework, all the data sets arriving at the same unit time are partitioned into subsets with the nearly best partition size and processed in parallel. To select the nearly best partition size for the stream data sets efficiently, we adopt bisection method strategy and inverse distance weighted strategy. Extreme learning machine, which is a fast and accurate machine learning method based on matrix calculating, is used to test the efficiency of our proposals. According to evaluation results, the throughputs based on SASDC-Framework are 8–35 times higher than those based on SDC-Topology and the best throughput is more than 40000 prediction requests per second in our environment. [ABSTRACT FROM AUTHOR]

In this era of big data, analyzing large scale data efficiently and accurately has become a challenging problem. As one of the ELM variants, online sequential extreme learning machine (OS-ELM) provides a method to analyze incremental data. Ensemble methods provide a way to learn from data more accurately. MapReduce, which provides a simple, scalable and fault-tolerant framework, can be utilized for large scale learning. In this paper, we first propose an ensemble OS-ELM framework which supports any combination of bagging, subspace partitioning and cross validation. Then we design a parallel ensemble of online sequential extreme learning machine (PEOS-ELM) algorithm based on MapReduce for large scale learning. PEOS-ELM algorithm is evaluated with real and synthetic data with the maximum number of training data 5120K and the maximum number of attributes 512. The speedup of this algorithm reaches as high as 40 on a cluster with maximum 80 cores. The accuracy of PEOS-ELM algorithm is at the same level as that of ensemble OS-ELM executing on a single machine, which is higher than that of the original OS-ELM. [ABSTRACT FROM AUTHOR]

In this age of big data, analyzing big data is a very challenging problem. MapReduce is a simple, scalable and fault-tolerant data processing framework that enables us to process a massive volume of data. Many machine learning algorithms have been designed based on MapReduce, but there are only a few works related to parallel extreme learning machine (ELM) which is a fast and accurate learning algorithm. Online sequential extreme learning machine (OS-ELM) is one of improved ELM algorithms to support online sequential learning efficiently. In this paper, we first analyze the dependency relationships of matrix calculations of OS-ELM, then propose a parallel online sequential extreme learning machine (POS-ELM) based on MapReduce. POS-ELM is evaluated with real and synthetic data with the maximum number of training data 1280 K and the maximum number of attributes 128. The experimental results show that the training accuracy and testing accuracy of POS-ELM are at the same level as those of OS-ELM and ELM, and it has good scalability with regard to the number of training data and the number of attributes. Compared to original ELM and OS-ELM where the capability to process large scale data is bounded by the limitation of resources within a single processing unit, POS-ELM can deal with much larger scale data. The larger the number of training data is, the higher the speedup of POS-ELM is. It can be concluded that POS-ELM has more powerful capability than both ELM and OS-ELM for large scale learning. [ABSTRACT FROM AUTHOR]