Utilization of Metadata and Data Models to Enhance Machine Learning

M. Gorai, M. Nene
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引用次数: 2

Abstract

Data plays the most significant role to attain efficiency in performing a task using Machine Learning (ML) techniques. Metadata (MD) represents data of data. MD extraction and data attribute selection play a vital role in defining the performance of ML models. The study in this paper focuses on the role of MD, data attributes and data models that define the learning capability of ML to evolve with human-like capability to learn and draw inferences. To evolve with such artificially intelligent autonomous systems, the study in this paper is a preliminary step towards applying ML techniques on textual data for performing syntactic analysis, further to evolve with semantic and behavioral analysis. Based on the rigorous survey study and observations, this paper concludes with the description of the parameters to quantify the performance of ML model which are essential to define the performance characteristics of ML. The increased deployment of ML is observed in the recent Artificial Intelligence arena, and hence the study contributes towards evolving performance parameters in applications that employ ML techniquestextbf.
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利用元数据和数据模型增强机器学习
数据在使用机器学习(ML)技术执行任务时发挥着最重要的作用。元数据(MD)表示数据的数据。MD提取和数据属性选择在定义机器学习模型的性能方面起着至关重要的作用。本文的研究重点是MD的作用、数据属性和数据模型,它们定义了ML的学习能力,使其具有类似人类的学习和推理能力。为了与这样的人工智能自治系统一起发展,本文的研究是将ML技术应用于文本数据进行语法分析的初步步骤,进一步与语义和行为分析一起发展。基于严格的调查研究和观察,本文最后描述了用于量化ML模型性能的参数,这些参数对于定义ML的性能特征至关重要。在最近的人工智能领域观察到ML的部署增加,因此该研究有助于在采用ML技术的应用程序中发展性能参数。
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