发表机构
Universidad Nacional de Tres de Febrero; Instituto Universitário de Lisboa (ISCTE-IUL); ISTAR-IUL; ALGORITMI Research Centre; University of Minho; Dataxplore; Boldt Gaming（阿根廷 Tres de Febrero 国立大学; 里斯本大学学院; ISTAR-IUL 研究所; ALGORITMI 研究中心; 米尼奥大学; Dataxplore; Boldt Gaming）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过银行电话营销案例对比了互信息与基于数据的敏感性分析两种特征选择方法的优劣，发现二者在不同假阳性场景下各有优势，互信息仍为有效方法且敏感性分析可用更少特征取得良好预测结果。
AI 中文摘要
特征选择是数据驱动知识发现项目中高度相关的任务，已开发出多种技术用于寻找对预测结果影响最大的特征，包括互信息（mutual information）以及近年来发展的基于数据的敏感性分析。本研究通过将两种技术应用于银行电话营销案例，分析了它们各自的优缺点。之后，分别利用两种技术识别出的对电话营销联系成功影响最大的特征集构建了逻辑回归（logistic regression）模型：互信息确定的特征集共13个特征，基于数据的敏感性分析确定的特征集共9个特征。结果显示，在较低的假阳性值下，基于数据的敏感性分析表现更好；而在较高的假阳性率下，互信息表现略优。因此，如果银行管理者希望在不冒损失大量成功案例风险的前提下略微降低联系成本，互信息会是更好的选择。这些结果表明，尽管互信息并非新方法，但它仍然是有效的特征选择方法；另一方面，基于数据的敏感性分析选择的特征集用更少的特征取得了良好的预测结果。
英文摘要
Feature selection is a highly relevant task in a data-driven knowledge discovery project. Several techniques have been developed aiming at finding the features that influence most an outcome to predict, including mutual information and, in recent years, the data-based sensitivity analysis. The present research focus on analyzing the advantages and disadvantages of each of these two techniques, by applying both to a bank telemarketing case. Thereafter, a logistic regression model is built on the tuned set of features identified by each of the two techniques as the most influencing set of features on the success of a telemarketing contact, in a total of 13 features for mutual information and 9 features for the data-based sensitivity analysis. The latter performs better for lower values of false positives while the former is slightly better for a higher false positive ratio. Thus, mutual information becomes a better choice if bank managers intend to reduce slightly the cost of contacts without risking losing a high number of successes. Such results show that mutual information, although not recent, is still a valid method for feature selection. On the other side, the data-based sensitivity analysis selection achieved good prediction results with less features.
Journal refBarraza, N., Moro, S., Ferreyra, M., & de la Peña, A. (2019). Mutual information and sensitivity analysis for feature selection in customer targeting: A comparative study. Journal of Information Science, 45(1), 53-67
