Framework of Intelligent System for Machine Learning Algorithm Selection in Social Sciences

D. Oreški
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引用次数: 2

Abstract

The ability to generate data has never been as powerful as today when three quintile bytes of data are generated daily. In the field of machine learning, a large number of algorithms have been developed, which can be used for intelligent data analysis and to solve prediction and descriptive problems in different domains. Developed algorithms have different effects on different problems.If one algorithmworks better on one dataset,the same algorithm may work worse on another data set. The reason is that each dataset has different features in terms of local and global characteristics. It is therefore imperative to know intrinsic algorithms behavior on different types of datasets andchoose the right algorithm for the problem solving. To address this problem, this papergives scientific contribution in meta learning field by proposing framework for identifying the specific characteristics of datasets in two domains of social sciences:education and business and develops meta models based on: ranking algorithms, calculating correlation of ranks, developing a multi-criteria model, two-component index and prediction based on machine learning algorithms. Each of the meta models serve as the basis for the development of intelligent system version. Application of such framework should include a comparative analysis of a large number of machine learning algorithms on a large number of datasetsfromsocial sciences.
社会科学机器学习算法选择的智能系统框架
生成数据的能力从未像今天这样强大,每天生成五分之三字节的数据。在机器学习领域,已经开发了大量的算法,可以用于智能数据分析,解决不同领域的预测和描述问题。开发的算法对不同的问题有不同的效果。如果一种算法在一个数据集上工作得更好,那么同样的算法在另一个数据集上可能工作得更差。原因是每个数据集在局部和全局特征方面具有不同的特征。因此,必须了解不同类型数据集上的内在算法行为,并选择正确的算法来解决问题。为了解决这一问题,本文在元学习领域做出了科学贡献,提出了识别教育和商业两个社会科学领域数据集具体特征的框架,并基于排名算法、计算排名相关性、开发多标准模型、双成分指数和基于机器学习算法的预测开发了元模型。每个元模型都是智能系统版本开发的基础。这种框架的应用应该包括对大量社会科学数据集上的大量机器学习算法的比较分析。
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