ORAC

奥拉克

• 批准号: 710661
• 金额: $ 12.2万
• 依托单位: PLKD0907 LIMITED
• 依托单位国家: 英国
• 项目类别: GRD Proof of Concept
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=710661
• 关键词: ORAC

The aim of the ORAC project is to research the use of machine learning, specifically neuralnetworks, techniques to significantly improve the accuracy of data classification within aspend analysis system. Spend analysis enables effective cost reduction and suppliermanagement by providing detailed all-round insight into procurement spend. Currently thestate-of-the art for automated data classification in such systems is 50%-70% accuracy (notlimited to machine learning approaches but typically using a combination of techniques) withconsiderable human intervention required to achieve 100% (this assumes the classification iscapable of addressing a wide range of market sectors). Initial investigation has indicated thatthe use of neural networks to pre-process the data could increase the automated accuracy to95%.The objectives of this project are to: 1) identify the commercial neural network technologiesthat can be adopted (Google’s technology being the preferred solution at present); 2) tailor theselected Neural Network (NN) to support the specific text structures used in spend analysis; 3)create a proof-of-concept demonstrator that will be used to characterize the capabilities of thenew categorization system (the new NN combined with the current Spend360 solution); 4)evaluate the capabilities and constraints of the NN-based solution and identify the issues thatneed to be addressed as part of a prototype evaluation activity.The primary benefit that will accrue from this new innovative approach is that it willsignificantly reduce the human involvement in the classification process and associated dataquality assurance. This will significantly reduce the time to complete the classification andimprove the accuracy. Together, this will result in cost reduction of the solution therebyenabling adoption by a range of organisations that find the current service cost too high.

ORAC项目的目的是研究机器学习的使用，特别是神经网络技术，以显着提高aspend分析系统中数据分类的准确性。支出分析通过提供对采购支出的详细全面洞察，实现有效的成本降低和供应商管理。目前，此类系统中自动数据分类的最新技术水平是50%-70%的准确率（不限于机器学习方法，但通常使用技术组合），需要相当多的人为干预才能实现100%（这假设分类能够解决广泛的市场领域）。初步研究表明，利用神经网络对数据进行预处理，可以将自动化的准确率提高到95%，本项目的目标是：1）确定可以采用的商业神经网络技术（Google的技术是目前的首选解决方案）; 2）定制所选择的神经网络（NN）以支持在花费分析中使用的特定文本结构; 3）创建一个概念验证演示器，用于描述新分类系统的功能（新NN与当前Spend 360解决方案相结合）; 4）评估NN的能力和约束-的解决方案，并确定需要解决的问题，作为原型评估活动的一部分。这种新的创新方法的主要好处是它将大大减少人类参与分类过程和相关的数据质量保证。这将大大减少完成分类的时间，提高分类的准确性.总之，这将导致解决方案的成本降低，从而使一系列发现当前服务成本过高的组织能够采用。