17,037 research outputs found
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
Learning to Identify Ambiguous and Misleading News Headlines
Accuracy is one of the basic principles of journalism. However, it is
increasingly hard to manage due to the diversity of news media. Some editors of
online news tend to use catchy headlines which trick readers into clicking.
These headlines are either ambiguous or misleading, degrading the reading
experience of the audience. Thus, identifying inaccurate news headlines is a
task worth studying. Previous work names these headlines "clickbaits" and
mainly focus on the features extracted from the headlines, which limits the
performance since the consistency between headlines and news bodies is
underappreciated. In this paper, we clearly redefine the problem and identify
ambiguous and misleading headlines separately. We utilize class sequential
rules to exploit structure information when detecting ambiguous headlines. For
the identification of misleading headlines, we extract features based on the
congruence between headlines and bodies. To make use of the large unlabeled
data set, we apply a co-training method and gain an increase in performance.
The experiment results show the effectiveness of our methods. Then we use our
classifiers to detect inaccurate headlines crawled from different sources and
conduct a data analysis.Comment: Accepted by IJCAI 201
Fast DD-classification of functional data
A fast nonparametric procedure for classifying functional data is introduced.
It consists of a two-step transformation of the original data plus a classifier
operating on a low-dimensional hypercube. The functional data are first mapped
into a finite-dimensional location-slope space and then transformed by a
multivariate depth function into the -plot, which is a subset of the unit
hypercube. This transformation yields a new notion of depth for functional
data. Three alternative depth functions are employed for this, as well as two
rules for the final classification on . The resulting classifier has
to be cross-validated over a small range of parameters only, which is
restricted by a Vapnik-Cervonenkis bound. The entire methodology does not
involve smoothing techniques, is completely nonparametric and allows to achieve
Bayes optimality under standard distributional settings. It is robust,
efficiently computable, and has been implemented in an R environment.
Applicability of the new approach is demonstrated by simulations as well as a
benchmark study
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