Conditional independence testing (CIT) is a common task in machine learning,
e.g., for variable selection, and a main component of constraint-based causal
discovery. While most current CIT approaches assume that all variables are
numerical or all variables are categorical, many real-world applications
involve mixed-type datasets that include numerical and categorical variables.
Non-parametric CIT can be conducted using conditional mutual information (CMI)
estimators combined with a local permutation scheme. Recently, two novel CMI
estimators for mixed-type datasets based on k-nearest-neighbors (k-NN) have
been proposed. As with any k-NN method, these estimators rely on the definition
of a distance metric. One approach computes distances by a one-hot encoding of
the categorical variables, essentially treating categorical variables as
discrete-numerical, while the other expresses CMI by entropy terms where the
categorical variables appear as conditions only. In this work, we study these
estimators and propose a variation of the former approach that does not treat
categorical variables as numeric. Our numerical experiments show that our
variant detects dependencies more robustly across different data distributions
and preprocessing types.

条件独立性检验是机器学习中的常见任务，本文研究了混合类型数据集下基于 k 最近邻的 CMI 估计器，并提出了一种新的方法，该方法不将类别变量视为数值变量，数值实验结果显示我们的变体能更稳定地检测不同数据分布和预处理类型下的依赖关系。

混合连续 - 分类变量的非参数条件独立性检验：一种新方法和数值评估

Non-parametric Conditional Independence Testing for Mixed  Continuous-Categorical Variables: A Novel Method and Numerical Evaluation

We extended dynamic time warping (DTW) into interval-based dynamic time
warping (iDTW), including (A) interval-based representation (iRep): [1]
abstracting raw, time-stamped data into interval-based abstractions, [2]
comparison-period scoping, [3] partitioning abstract intervals into a given
temporal granularity; (B) interval-based matching (iMatch): matching
partitioned, abstract-concepts records, using a modified DTW. Using domain
knowledge, we abstracted the raw data of medical records, for up to three
concepts out of four or five relevant concepts, into two interval types: State
abstractions (e.g. LOW, HIGH) and Gradient abstractions (e.g. INCREASING,
DECREASING). We created all uni-dimensional (State or Gradient) or
multi-dimensional (State and Gradient) abstraction combinations. Tasks:
Classifying 161 oncology patients records as autologous or allogenic
bone-marrow transplantation; classifying 125 hepatitis patients records as B or
C hepatitis; predicting micro- or macro-albuminuria in the next year for 151
Type 2 diabetes patients. We used a k-Nearest-Neighbors majority, k = an odd
number from 1 to SQRT(N), N = set size. 75,936 10-fold cross-validation
experiments were performed: 33,600 (Oncology), 28,800 (Hepatitis), 13,536
(Diabetes). Measures: Area Under the Curve (AUC), optimal Youden's Index.
Paired t-tests compared result vectors for equivalent configurations other than
a tested variable, to determine a significant mean accuracy difference
(P<0.05). Mean classification and prediction using abstractions was
significantly better than using only raw time-stamped data. In each domain, at
least one abstraction combination led to a significantly better mean
performance than raw data. Increasing feature number and using
Multi-dimensional abstractions enhanced performance. Unlike when using raw
data, optimal mean performance was often reached with k=5, using abstractions.

该研究使用区间动态时间规整法（iDTW）从医学记录中提取数据特征，实现肿瘤、乙肝和糖尿病患者记录的分类和预测，并比较使用抽象数据与原始时间戳数据间的性能差异。

一种基于多元抽象和区间动态时间扭曲的相似性测量方法，在纵向医疗记录中的实现和评估

Implementation and Evaluation of a Multivariate Abstraction-Based,  Interval-Based Dynamic Time-Warping Method as a Similarity Measure for  Longitudinal Medical Records

Missing data imputation can help improve the performance of prediction models
in situations where missing data hide useful information. This paper compares
methods for imputing missing categorical data for supervised classification
tasks. We experiment on two machine learning benchmark datasets with missing
categorical data, comparing classifiers trained on non-imputed (i.e., one-hot
encoded) or imputed data with different levels of additional missing-data
perturbation. We show imputation methods can increase predictive accuracy in
the presence of missing-data perturbation, which can actually improve
prediction accuracy by regularizing the classifier. We achieve the
state-of-the-art on the Adult dataset with missing-data perturbation and
k-nearest-neighbors (k-NN) imputation.

本文比较了不同的方法在监督分类任务中填补缺失分类数据的效果，并证明了方法可以提高预测准确性，并达到了 Missing-data perturbation 和 k-nearest-neighbors imputation 下准确率的最新水平。