通过特征贡献和 MDI 特征重要性解读深林

IF 4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Knowledge Discovery from Data Pub Date : 2024-01-19 DOI:10.1145/3641108
Yi-Xiao He, Shen-Huan Lyu, Yuan Jiang
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引用次数: 0

摘要

深度森林是一种无差别深度模型,在广泛的应用领域,特别是分类/符号或混合建模任务中取得了令人印象深刻的实证成功。许多应用领域更青睐可解释模型,如具有特征贡献的随机森林,它可以为每个预测提供局部解释,而均值递减杂质(MDI)可以提供全局特征重要性。然而,深度森林作为随机森林的级联,只在第一层具有可解释性。从第二层开始,许多树的分裂发生在前一层产生的新特征上,这使得现有的随机森林解释工具变得不适用。为了揭示原始特征对深层的影响,我们设计了一种计算方法,每一层都有一个估计步骤和校准步骤,并提出了我们的深层森林特征贡献和 MDI 特征重要性计算工具。在模拟数据和真实世界数据上的实验结果验证了我们方法的有效性。
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Interpreting Deep Forest through Feature Contribution and MDI Feature Importance

Deep forest is a non-differentiable deep model that has achieved impressive empirical success across a wide variety of applications, especially on categorical/symbolic or mixed modeling tasks. Many of the application fields prefer explainable models, such as random forests with feature contributions that can provide a local explanation for each prediction, and Mean Decrease Impurity (MDI) that can provide global feature importance. However, deep forest, as a cascade of random forests, possesses interpretability only at the first layer. From the second layer on, many of the tree splits occur on the new features generated by the previous layer, which makes existing explaining tools for random forests inapplicable. To disclose the impact of the original features in the deep layers, we design a calculation method with an estimation step followed by a calibration step for each layer, and propose our feature contribution and MDI feature importance calculation tools for deep forest. Experimental results on both simulated data and real-world data verify the effectiveness of our methods.

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来源期刊
ACM Transactions on Knowledge Discovery from Data
ACM Transactions on Knowledge Discovery from Data COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
6.70
自引率
5.60%
发文量
172
审稿时长
3 months
期刊介绍: TKDD welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include, but are not limited to: scalable and effective algorithms for data mining and big data analysis, mining brain networks, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology including massively parallel processing and cloud computing platforms. TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices. TKDD also encourages papers that describe emerging data mining applications that cannot be satisfied by the current data mining technology.
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