140 research outputs found
Food Ingredients Recognition through Multi-label Learning
Automatically constructing a food diary that tracks the ingredients consumed
can help people follow a healthy diet. We tackle the problem of food
ingredients recognition as a multi-label learning problem. We propose a method
for adapting a highly performing state of the art CNN in order to act as a
multi-label predictor for learning recipes in terms of their list of
ingredients. We prove that our model is able to, given a picture, predict its
list of ingredients, even if the recipe corresponding to the picture has never
been seen by the model. We make public two new datasets suitable for this
purpose. Furthermore, we prove that a model trained with a high variability of
recipes and ingredients is able to generalize better on new data, and visualize
how it specializes each of its neurons to different ingredients.Comment: 8 page
On Aggregation in Ensembles of Multilabel Classifiers
While a variety of ensemble methods for multilabel classification have been
proposed in the literature, the question of how to aggregate the predictions of
the individual members of the ensemble has received little attention so far. In
this paper, we introduce a formal framework of ensemble multilabel
classification, in which we distinguish two principal approaches: "predict then
combine" (PTC), where the ensemble members first make loss minimizing
predictions which are subsequently combined, and "combine then predict" (CTP),
which first aggregates information such as marginal label probabilities from
the individual ensemble members, and then derives a prediction from this
aggregation. While both approaches generalize voting techniques commonly used
for multilabel ensembles, they allow to explicitly take the target performance
measure into account. Therefore, concrete instantiations of CTP and PTC can be
tailored to concrete loss functions. Experimentally, we show that standard
voting techniques are indeed outperformed by suitable instantiations of CTP and
PTC, and provide some evidence that CTP performs well for decomposable loss
functions, whereas PTC is the better choice for non-decomposable losses.Comment: 14 pages, 2 figure
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
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning
Being able to model correlations between labels is considered crucial in
multi-label classification. Rule-based models enable to expose such
dependencies, e.g., implications, subsumptions, or exclusions, in an
interpretable and human-comprehensible manner. Albeit the number of possible
label combinations increases exponentially with the number of available labels,
it has been shown that rules with multiple labels in their heads, which are a
natural form to model local label dependencies, can be induced efficiently by
exploiting certain properties of rule evaluation measures and pruning the label
search space accordingly. However, experiments have revealed that multi-label
heads are unlikely to be learned by existing methods due to their
restrictiveness. To overcome this limitation, we propose a plug-in approach
that relaxes the search space pruning used by existing methods in order to
introduce a bias towards larger multi-label heads resulting in more expressive
rules. We further demonstrate the effectiveness of our approach empirically and
show that it does not come with drawbacks in terms of training time or
predictive performance.Comment: Preprint version. To appear in Proceedings of the 22nd International
Conference on Discovery Science, 201
Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules
Exploiting dependencies between labels is considered to be crucial for
multi-label classification. Rules are able to expose label dependencies such as
implications, subsumptions or exclusions in a human-comprehensible and
interpretable manner. However, the induction of rules with multiple labels in
the head is particularly challenging, as the number of label combinations which
must be taken into account for each rule grows exponentially with the number of
available labels. To overcome this limitation, algorithms for exhaustive rule
mining typically use properties such as anti-monotonicity or decomposability in
order to prune the search space. In the present paper, we examine whether
commonly used multi-label evaluation metrics satisfy these properties and
therefore are suited to prune the search space for multi-label heads.Comment: Preprint version. To appear in: Proceedings of the Pacific-Asia
Conference on Knowledge Discovery and Data Mining (PAKDD) 2018. See
http://www.ke.tu-darmstadt.de/bibtex/publications/show/3074 for further
information. arXiv admin note: text overlap with arXiv:1812.0005
Ontology of core data mining entities
In this article, we present OntoDM-core, an ontology of core data mining
entities. OntoDM-core defines themost essential datamining entities in a three-layered
ontological structure comprising of a specification, an implementation and an application
layer. It provides a representational framework for the description of mining
structured data, and in addition provides taxonomies of datasets, data mining tasks,
generalizations, data mining algorithms and constraints, based on the type of data.
OntoDM-core is designed to support a wide range of applications/use cases, such as
semantic annotation of data mining algorithms, datasets and results; annotation of
QSAR studies in the context of drug discovery investigations; and disambiguation of
terms in text mining. The ontology has been thoroughly assessed following the practices
in ontology engineering, is fully interoperable with many domain resources and
is easy to extend
Automated Selection and Configuration of Multi-Label Classification Algorithms with Grammar-Based Genetic Programming
This paper proposes Auto-MEKAGGP, an Automated Machine Learning (Auto-ML) method for Multi-Label Classification (MLC) based on the MEKA tool, which offers a number of MLC algorithms. In
MLC, each example can be associated with one or more class labels, making MLC problems harder than conventional (single-label) classification problems. Hence, it is essential to select an MLC algorithm and its configuration tailored (optimized) for the input dataset. Auto-MEKAGGP addresses this problem with two key ideas. First, a large number of choices of MLC algorithms and configurations from MEKA are represented into a grammar. Second, our proposed Grammar-based Genetic Programming (GGP) method uses that grammar to search for the best MLC algorithm and configuration for the input dataset. Auto-MEKAGGP was tested in 10 datasets and compared to two well-known MLC methods, namely Binary Relevance and Classifier Chain, and also compared to GA-AutoMLC, a genetic algorithm we recently proposed for the same task. Two versions of Auto-MEKAGGP were tested: a full version with the proposed grammar, and a simplified version where the grammar includes only the algorithmic components used by GA-Auto-MLC. Overall, the full version of Auto-MEKAGGP achieved the best predictive accuracy among all five evaluated methods, being the winner in six out of the 10 datasets
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
A two-step target binding and selectivity support vector machines approach for virtual screening of dopamine receptor subtype-selective ligands
10.1371/journal.pone.0039076PLoS ONE76
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