In the process of funding research projects, two important factors must be studied. First, experts judges the potential value of a project. Secondly, the research ability is judged by the applicants previous research activity. The most appropriate way to assign the appropriate amount of money to project proposals is always a difficult decision. This work focuses on the second factor based on classifying the researchers previous research activity on an automated logical classification (accepted, rejected) resolving conflicts of interests between administration and applicants and helping in the decision-making process. As the class in these kinds of studies is usually unbalanced, because there are fewer accepted projects than rejected projects, how the use of an imbalanced dataset or a balanced dataset affects to the models is investigated by using several resampling methods. Later, several trees and rule-based machine learning techniques are used to create classification models. This is based on information from the faculty members information of the “Technical Particular University of Loja (UTPL),” in cases, with balanced datasets and those with unbalanced datasets. Multivariate analysis, feature selection, algorithm parameter tuning and validation methods are used to achieve robust classification models. The most accurate results are obtained with a rules-based model and use of the C5.0 algorithm. As the latter provides acceptable accuracy, close to 95 % when predicting both classes and to 99 % when predicting the accepted projects class, both the methodology and final model are validated.

在为研究项目提供资金的过程中，必须研究两个重要因素。首先，专家判断项目的潜在价值。其次，研究能力由申请人先前的研究活动来判断。向项目建议分配适当金额的最适当方法始终是一个困难的决定。这项工作的重点是基于对研究人员先前的研究活动进行自动逻辑分类（接受，拒绝）的第二个因素，以解决管理者与申请人之间的利益冲突并为决策过程提供帮助。由于这类研究的班级通常是不平衡的，因为接受的项目少于拒绝的项目，通过使用几种重采样方法，研究了不平衡数据集或平衡数据集的使用如何影响模型。后来，使用了几棵树和基于规则的机器学习技术来创建分类模型。在具有平衡数据集和非平衡数据集的情况下，这是基于“洛哈技术特别大学（UTPL）”教职员工的信息。多变量分析，特征选择，算法参数调整和验证方法用于获得鲁棒的分类模型。使用基于规则的模型并使用C5.0算法可获得最准确的结果。由于后者提供了可接受的准确性，因此在预测两个类别时都接近95％，在预测接受的项目类别时接近99％，