  Deep neural networks (DNNs) have been shown to outperform traditional machine
learning algorithms in a broad variety of application domains due to their
effectiveness in modeling complex problems and handling high-dimensional
datasets. Many real-life datasets, however, are of increasingly high
dimensionality, where a large number of features may be irrelevant for both
supervised and unsupervised learning tasks. The inclusion of such features
would not only introduce unwanted noise but also increase computational
complexity. Furthermore, due to high non-linearity and dependency among a large
number of features, DNN models tend to be unavoidably opaque and perceived as
black-box methods because of their not well-understood internal functioning.
Their algorithmic complexity is often simply beyond the capacities of humans to
understand the interplay among myriads of hyperparameters. A well-interpretable
model can identify statistically significant features and explain the way they
affect the model's outcome. In this paper, we propose an efficient method to
improve the interpretability of black-box models for classification tasks in
the case of high-dimensional datasets. First, we train a black-box model on a
high-dimensional dataset to learn the embeddings on which the classification is
performed. To decompose the inner working principles of the black-box model and
to identify top-k important features, we employ different probing and
perturbing techniques. We then approximate the behavior of the black-box model
by means of an interpretable surrogate model on the top-k feature space.
Finally, we derive decision rules and local explanations from the surrogate
model to explain individual decisions. Our approach outperforms
state-of-the-art methods like TabNet and XGboost when tested on different
datasets with varying dimensionality between 50 and 20,000 w.r.t metrics and
explainability.
