Taking classifier learning as an example, dealing with incomplete data is an important issue, since data incompleteness not only impacts interpretations of the data or the models created from the data but may also affect the prediction accuracy of learned classifiers.
The testing results show that the proposed VaFALCON is able to improve the system accuracy up to 84.5% and recover accuracy at least 81% even under the severe data incompleteness case, where all datasets of the database are incomplete.
In this paper, our work follows the kernel-based approach with vector quantization and the nearest prototype strategy by proposing a robust and effective incomplete educational data clustering algorithm, named VQ_fk_nps, where we tackle data incompleteness in the data space while doing clustering in the feature space.
Bottom : Data incompleteness.
A prototype project of data summarization ontology-based Query by Examples (QBE) for summarizing the data incompleteness demonstrates the effectiveness of the proposed framework.
The second presents the novel concept of an Interval-Valued Fuzzy Classifier which is able to operate under data incompleteness and uncertainty.
THE most daunting challenges in making sense of social media are data incompleteness and noise: not knowing whether you have all the information, and how to sort out what's relevant.
Such data incompleteness is the peculiarity that makes our task challenging.
The core features of Big Data include data-size, data incompleteness, data incompatibility, data heterogeneity and incongruent sampling.
Unfortunately, no existing educational data mining work has taken data incompleteness into account thoroughly except for [24].
To tackle the challenges associated with data incompleteness, Chen and Lin [13] investigated to apply the advanced deep learning methods to handle noisy data and tolerate some messiness.
