Approximate Decision Tree-Based Multiple Classifier Systems

Barbareschi, Mario; Papa, Cristina; Sansone, Carlo

doi:10.1007/978-3-319-67308-0_5

Cited by 1 publication

(2 citation statements)

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“…In [14], a similar approach to the one presented in this paper has been adopted, but instead of exploring the solutions space with heuristics, the use of an exact algorithm, namely Branch & Bound (B&B), was proposed. While, on the one hand, the use of an exact algorithm for the solution of a MOP allows to reach a global optimal solution, on the other hand its use becomes prohibitive with large solutions spaces.…”

Section: Comparison With Previous Approachesmentioning

confidence: 99%

“…Experimental evidences, conducted over a significant dataset, highlight the efficacy of the approach by exploring approximate DTs variants by means of a genetic algorithm (GA) and by synthesizing hardware accelerator on a Xilinx Zynq 7020 FPGA device. Additionally, we compare our experimental result against exhaustive branch and bound approach proposed in [14] demonstrating a reduction in area occupancy of about 10%.…”

Section: Introductionmentioning

confidence: 97%

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Advancing synthesis of decision tree-based multiple classifier systems: an approximate computing case study

2021

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So far, multiple classifier systems have been increasingly designed to take advantage of hardware features, such as high parallelism and computational power. Indeed, compared to software implementations, hardware accelerators guarantee higher throughput and lower latency. Although the combination of multiple classifiers leads to high classification accuracy, the required area overhead makes the design of a hardware accelerator unfeasible, hindering the adoption of commercial configurable devices. For this reason, in this paper, we exploit approximate computing design paradigm to trade hardware area overhead off for classification accuracy. In particular, starting from trained DT models and employing precision-scaling technique, we explore approximate decision tree variants by means of multiple objective optimization problem, demonstrating a significant performance improvement targeting field-programmable gate array devices.

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Section: Comparison With Previous Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%