Robust feature selection is vital for creating reliable and interpretable
Machine Learning (ML) models. When designing statistical prediction models in
cases where domain knowledge is limited and underlying interactions are
unknown, choosing the optimal set of features is often difficult. To mitigate
this issue, we introduce a Multidata (M) causal feature selection approach that
simultaneously processes an ensemble of time series datasets and produces a
single set of causal drivers. This approach uses the causal discovery
algorithms PC1 or PCMCI that are implemented in the Tigramite Python package.
These algorithms utilize conditional independence tests to infer parts of the
causal graph. Our causal feature selection approach filters out
causally-spurious links before passing the remaining causal features as inputs
to ML models (Multiple linear regression, Random Forest) that predict the
targets. We apply our framework to the statistical intensity prediction of
Western Pacific Tropical Cyclones (TC), for which it is often difficult to
accurately choose drivers and their dimensionality reduction (time lags,
vertical levels, and area-averaging). Using more stringent significance
thresholds in the conditional independence tests helps eliminate spurious
causal relationships, thus helping the ML model generalize better to unseen TC
cases. M-PC1 with a reduced number of features outperforms M-PCMCI, non-causal
ML, and other feature selection methods (lagged correlation, random), even
slightly outperforming feature selection based on eXplainable Artificial
Intelligence. The optimal causal drivers obtained from our causal feature
selection help improve our understanding of underlying relationships and
suggest new potential drivers of TC intensification.

通过提出多数据因果特征选择方法，该方法利用 Tigramite Python 包实现的 PC1 或 PCMCI 因果发现算法，对多个时间序列数据集进行处理，过滤出有因果关系和值得关注的变量进行输入，从而有效地预测了西太平洋热带气旋的统计强度。

使用多数据因果发现选择机器学习应用的稳健特征

Selecting Robust Features for Machine Learning Applications using  Multidata Causal Discovery

Adjective phrases like "a little bit surprised", "completely shocked", or
"not stunned at all" are not handled properly by currently published
state-of-the-art emotion classification and intensity prediction systems which
use pre-dominantly non-contextualized word embeddings as input. Based on this
finding, we analyze differences between embeddings used by these systems in
regard to their capability of handling such cases. Furthermore, we argue that
intensifiers in context of emotion words need special treatment, as is
established for sentiment polarity classification, but not for more
fine-grained emotion prediction. To resolve this issue, we analyze different
aspects of a post-processing pipeline which enriches the word representations
of such phrases. This includes expansion of semantic spaces at the phrase level
and sub-word level followed by retrofitting to emotion lexica. We evaluate the
impact of these steps with A La Carte and Bag-of-Substrings extensions based on
pretrained GloVe, Word2vec, and fastText embeddings against a crowd-sourced
corpus of intensity annotations for tweets containing our focus phrases. We
show that the fastText-based models do not gain from handling these specific
phrases under inspection. For Word2vec embeddings, we show that our
post-processing pipeline improves the results by up to 8% on a novel dataset
densely populated with intensifiers.

针对当前主流的情感分类和强度预测系统不能很好地处理类似于「稍微惊讶」「完全震惊」或「一点也不震惊」等形容词短语的问题，本文分析了它们与常规词嵌入不同的地方，并选用不同的语义空间扩展和情感词典修正方法研究了后处理流程对其处理效果的影响，结果证明可显著提高情感词在种类更细致的分类任务中的正确率。