engleski

FPGA kernels for classification rule induction

Classification is one of the core tasks in machine learning data mining. One of several models of classification are classification rules, which use a set of if-then rules to describe a classification model. In this paper we present a set of FPGA-based compute kernels for accelerating classification rule induction. The kernels can be combined to perform specific procedures in rule induction process, such as evaluating rule coverage, or estimating out-of-bag-error. Since classification problems are getting increasingly larger, there is a need for faster implementations of classification rule induction. One of the platforms that offer great potential for accelerating data mining tasks is FPGA (field programmable gate array), which provides the means for implementing application specific accelerators.

classification rules ; FPGA ; dataflow ; machine learning

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