49,918 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
Playing for Data: Ground Truth from Computer Games
Recent progress in computer vision has been driven by high-capacity models
trained on large datasets. Unfortunately, creating large datasets with
pixel-level labels has been extremely costly due to the amount of human effort
required. In this paper, we present an approach to rapidly creating
pixel-accurate semantic label maps for images extracted from modern computer
games. Although the source code and the internal operation of commercial games
are inaccessible, we show that associations between image patches can be
reconstructed from the communication between the game and the graphics
hardware. This enables rapid propagation of semantic labels within and across
images synthesized by the game, with no access to the source code or the
content. We validate the presented approach by producing dense pixel-level
semantic annotations for 25 thousand images synthesized by a photorealistic
open-world computer game. Experiments on semantic segmentation datasets show
that using the acquired data to supplement real-world images significantly
increases accuracy and that the acquired data enables reducing the amount of
hand-labeled real-world data: models trained with game data and just 1/3 of the
CamVid training set outperform models trained on the complete CamVid training
set.Comment: Accepted to the 14th European Conference on Computer Vision (ECCV
2016
A review of associative classification mining
Associative classification mining is a promising approach in data mining that utilizes the
association rule discovery techniques to construct classification systems, also known as
associative classifiers. In the last few years, a number of associative classification algorithms
have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms
employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule
evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative
classification techniques with regards to the above criteria. Finally, future directions in associative
classification, such as incremental learning and mining low-quality data sets, are also
highlighted in this paper
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