16,054 research outputs found
Learning Interpretable Rules for Multi-label Classification
Multi-label classification (MLC) is a supervised learning problem in which,
contrary to standard multiclass classification, an instance can be associated
with several class labels simultaneously. In this chapter, we advocate a
rule-based approach to multi-label classification. Rule learning algorithms are
often employed when one is not only interested in accurate predictions, but
also requires an interpretable theory that can be understood, analyzed, and
qualitatively evaluated by domain experts. Ideally, by revealing patterns and
regularities contained in the data, a rule-based theory yields new insights in
the application domain. Recently, several authors have started to investigate
how rule-based models can be used for modeling multi-label data. Discussing
this task in detail, we highlight some of the problems that make rule learning
considerably more challenging for MLC than for conventional classification.
While mainly focusing on our own previous work, we also provide a short
overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models
in Computer Vision and Machine Learning. The Springer Series on Challenges in
Machine Learning. Springer (2018). See
http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further
informatio
Multi-label Rule Learning
Research on multi-label classification is concerned with developing and evaluating algorithms that learn a predictive model for the automatic assignment of data points to a subset of predefined class labels. This is in contrast to traditional classification settings, where individual data points cannot be assigned to more than a single class. As many practical use cases demand a flexible categorization of data, where classes must not necessarily be mutually exclusive, multi-label classification has become an established topic of machine learning research. Nowadays, it is used for the assignment of keywords to text documents, the annotation of multimedia files, such as images, videos, or audio recordings, as well as for diverse applications in biology, chemistry, social network analysis, or marketing. During the past decade, increasing interest in the topic has resulted in a wide variety of different multi-label classification methods. Following the principles of supervised learning, they derive a model from labeled training data, which can afterward be used to obtain predictions for yet unseen data. Besides complex statistical methods, such as artificial neural networks, symbolic learning approaches have not only been shown to provide state-of-the-art performance in many applications but are also a common choice in safety-critical domains that demand human-interpretable and verifiable machine learning models. In particular, rule learning algorithms have a long history of active research in the scientific community. They are often argued to meet the requirements of interpretable machine learning due to the human-legible representation of learned knowledge in terms of logical statements. This work presents a modular framework for implementing multi-label rule learning methods. It does not only provide a unified view of existing rule-based approaches to multi-label classification, but also facilitates the development of new learning algorithms. Two novel instantiations of the framework are investigated to demonstrate its flexibility. Whereas the first one relies on traditional rule learning techniques and focuses on interpretability, the second one is based on a generalization of the gradient boosting framework and focuses on predictive performance rather than the simplicity of models. Motivated by the increasing demand for highly scalable learning algorithms that are capable of processing large amounts of training data, this work also includes an extensive discussion of algorithmic optimizations and approximation techniques for the efficient induction of rules. As the novel multi-label classification methods that are presented in this work can be viewed as instantiations of the same framework, they can both benefit from most of these principles. Their effectiveness and efficiency are compared to existing baselines experimentally
A New Interpretable Neural Network-Based Rule Model for Healthcare Decision Making
In healthcare applications, understanding how machine/deep learning models
make decisions is crucial. In this study, we introduce a neural network
framework, (TT-rules), that combines the global
and exact interpretability properties of rule-based models with the high
performance of deep neural networks. TT-rules is built upon (TTnet), a family of deep neural networks initially developed for
formal verification. By extracting the necessary and sufficient rules
from the trained TTnet model (global interpretability) to yield
the same output as the TTnet (exact interpretability), TT-rules effectively
transforms the neural network into a rule-based model. This rule-based model
supports binary classification, multi-label classification, and regression
tasks for small to large tabular datasets. After outlining the framework, we
evaluate TT-rules' performance on healthcare applications and compare it to
state-of-the-art rule-based methods. Our results demonstrate that TT-rules
achieves equal or higher performance compared to other interpretable methods.
Notably, TT-rules presents the first accurate rule-based model capable of
fitting large tabular datasets, including two real-life DNA datasets with over
20K features.Comment: This work was presented at IAIM23 in Singapore https://iaim2023.sg/.
arXiv admin note: substantial text overlap with arXiv:2309.0963
Learning and Interpreting Multi-Multi-Instance Learning Networks
We introduce an extension of the multi-instance learning problem where
examples are organized as nested bags of instances (e.g., a document could be
represented as a bag of sentences, which in turn are bags of words). This
framework can be useful in various scenarios, such as text and image
classification, but also supervised learning over graphs. As a further
advantage, multi-multi instance learning enables a particular way of
interpreting predictions and the decision function. Our approach is based on a
special neural network layer, called bag-layer, whose units aggregate bags of
inputs of arbitrary size. We prove theoretically that the associated class of
functions contains all Boolean functions over sets of sets of instances and we
provide empirical evidence that functions of this kind can be actually learned
on semi-synthetic datasets. We finally present experiments on text
classification, on citation graphs, and social graph data, which show that our
model obtains competitive results with respect to accuracy when compared to
other approaches such as convolutional networks on graphs, while at the same
time it supports a general approach to interpret the learnt model, as well as
explain individual predictions.Comment: JML
Interpretable multiclass classification by MDL-based rule lists
Interpretable classifiers have recently witnessed an increase in attention
from the data mining community because they are inherently easier to understand
and explain than their more complex counterparts. Examples of interpretable
classification models include decision trees, rule sets, and rule lists.
Learning such models often involves optimizing hyperparameters, which typically
requires substantial amounts of data and may result in relatively large models.
In this paper, we consider the problem of learning compact yet accurate
probabilistic rule lists for multiclass classification. Specifically, we
propose a novel formalization based on probabilistic rule lists and the minimum
description length (MDL) principle. This results in virtually parameter-free
model selection that naturally allows to trade-off model complexity with
goodness of fit, by which overfitting and the need for hyperparameter tuning
are effectively avoided. Finally, we introduce the Classy algorithm, which
greedily finds rule lists according to the proposed criterion. We empirically
demonstrate that Classy selects small probabilistic rule lists that outperform
state-of-the-art classifiers when it comes to the combination of predictive
performance and interpretability. We show that Classy is insensitive to its
only parameter, i.e., the candidate set, and that compression on the training
set correlates with classification performance, validating our MDL-based
selection criterion
Interpretable Categorization of Heterogeneous Time Series Data
Understanding heterogeneous multivariate time series data is important in
many applications ranging from smart homes to aviation. Learning models of
heterogeneous multivariate time series that are also human-interpretable is
challenging and not adequately addressed by the existing literature. We propose
grammar-based decision trees (GBDTs) and an algorithm for learning them. GBDTs
extend decision trees with a grammar framework. Logical expressions derived
from a context-free grammar are used for branching in place of simple
thresholds on attributes. The added expressivity enables support for a wide
range of data types while retaining the interpretability of decision trees. In
particular, when a grammar based on temporal logic is used, we show that GBDTs
can be used for the interpretable classi cation of high-dimensional and
heterogeneous time series data. Furthermore, we show how GBDTs can also be used
for categorization, which is a combination of clustering and generating
interpretable explanations for each cluster. We apply GBDTs to analyze the
classic Australian Sign Language dataset as well as data on near mid-air
collisions (NMACs). The NMAC data comes from aircraft simulations used in the
development of the next-generation Airborne Collision Avoidance System (ACAS
X).Comment: 9 pages, 5 figures, 2 tables, SIAM International Conference on Data
Mining (SDM) 201
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