By Matthias Renz, Cyrus Shahabi, Xiaofang Zhou, Muhammad Aamir Cheema

This quantity set LNCS 9049 and LNCS 9050 constitutes the refereed complaints of the twentieth foreign convention on Database platforms for complex functions, DASFAA 2015, held in Hanoi, Vietnam, in April 2015.

The sixty three complete papers awarded have been rigorously reviewed and chosen from a complete of 287 submissions. The papers disguise the subsequent themes: info mining; facts streams and time sequence; database garage and index; spatio-temporal info; smooth computing platform; social networks; info integration and knowledge caliber; details retrieval and summarization; safety and privateness; outlier and imbalanced facts research; probabilistic and unsure information; question processing.

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Extra resources for Database Systems for Advanced Applications: 20th International Conference, DASFAA 2015, Hanoi, Vietnam, April 20-23, 2015, Proceedings, Part II

Example text

1. An example of Gaussian-distributed Minority and Majority Samples First, let us focus only on the minority class samples. The samples 1 and 3 are at the borderline of minority class, while the sample 2 is an interior point of minority class. As illustrated in Fig. 2, where each circle is centered at sample 1, sample 2, or sample 3, and the radius of each circle equals to the chosen cutoff value . Clearly, the local minority density of sample 1, ρ(1), equals to 5, because there are five minority class samples in its circle.

For an outlier however, missing one of the true nearest neighbors – which may be another outlier with low density – and instead taking an even farther object as neighbor actually increases the chance that we end up using a cluster member of a nearby cluster for comparison. So while the approximation will likely not affect inlier scores much, we can expect it to emphasize outliers. This effect is related to an observation for subsampling ensembles for outlier detection [42]: when subsampling a relative share of s objects from a uniformly distributed ball, the kNN-distances are expected to increase by a relative factor of (1 − s1/d )/s1/d .

The details of MOT2LD are described below. Table 1. The framework of MOT2LD algrorithm Algorithm: Minority Oversampling Technique based on Local Densities in Low-Dimensional Space Input: NSamples: A set of majority class samples (Negative class) PSamples: A set of minority class samples (Positive class) K: The number of nearest neighbors observed when filtering noise samples NumToGen: The number of synthetic minority samples to be generated Output: Y: The set of synthetic minority samples that are generated Procedure Begin Step 1: (Dimensionality Reduction) Use t-SNE algorithm to reduce the dimensionality of the dataset, where each data sample x is represented as a low-dimensional image in a low-dimensional space.

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