SBIR Phase I: Feature Engineering Automation via Human Insight Integration and End to End Optimization

这个小型企业创新研究(SBIR)第一阶段项目的更广泛影响是使拥有最低数学和编程知识的个人能够学习和构建人工智能，并通过这一成功打开了一条通往更多STEM参与和成功的道路。这一创新通过在完整的生命周期框架内自动执行AI生命周期的关键组件--功能工程(FE)来帮助实现自动化。行业可以更快、更低成本地将人工智能应用于业务解决方案，并与现有员工一起应用。通过专注于企业数据，这项技术使企业能够广泛使用，并随后获得经济效益。该小型企业创新研究(SBIR)第一阶段项目解决了商业人工智能解决方案的一个关键限制，即人工专家驱动和主要手动功能工程(FE)阶段。这一创新是在全生命周期环境中实现有限元自动化的一种新方法，具有三个不同之处：(A)通过自动生成和人工注释的知识图嵌入人类洞察力；(B)将复杂的有限元过程构建为一系列子阶段，用于动态组合创新，通过知识图洞察进一步调整；以及(C)通过端到端量化反馈进行迭代优化。该方法集成了专家的直觉，通过知识图和反馈实现了广泛的潜在有限元转换和可探索的状态空间。这项研究通过对照真实场景进行测试并利用直接暴露现实FE问题的客户数据集来确保翻译的可行性，解决了过去研究工作的一个重大限制。该项目努力证明该方法在解决生产企业的特征工程问题中的可行性和效益。预期的结果是一种可以很容易地商业化的解决方案，以使用数字、分类、文本和时间序列数据为广泛的业务用途实现功能工程的自动化。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to enable individuals with minimal mathematics and programming knowledge to learn and build AI, and through that success open a pathway to greater STEM engagement and success. This innovation helps by automating a key component of the AI lifecycle, Feature Engineering (FE), within a full lifecycle framework. Industries can apply AI to business solutions faster, at lower cost, and with existing employees. By focusing on enterprise data, this technology enables broad corporate use and subsequent economic benefit. This Small Business Innovation Research (SBIR) Phase I project addresses a key limitation of commercial AI solutions, the human expert driven and largely manual Feature Engineering (FE) stage. The innovation is a novel approach to FE automation within a full lifecycle context, with three differentiations: (a) embedding human insight via auto-generated and human annotated Knowledge Graphs, (b) structuring the complex FE process as a series of sub-stages for dynamic combination of innovations, further tuned via Knowledge Graph insight and (c) iterative optimization via quantitative end-to-end feedback. The approach integrates expert intuition, enabling a wide selection of potential FE transformations and explorable state space via the Knowledge Graph and feedback. The research ensures translational viability by testing against real-world scenarios and utilizing customer datasets that directly expose realistic FE problems, addressing a significant limitation of past research efforts. The project strives to prove the feasibility and benefits of this approach in tackling Feature Engineering problems in production enterprises. The anticipated result is a solution that can be easily commercialized to automate Feature Engineering for a wide range of business usages using numerical, categorical, text and time series data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.