By Jingrui He
In many real-world difficulties, infrequent different types (minority periods) play crucial roles regardless of their severe shortage. the invention, characterization and prediction of infrequent different types of infrequent examples may possibly safeguard us from fraudulent or malicious habit, relief medical discovery, or even shop lives.
This e-book specializes in infrequent classification research, the place the bulk sessions have gentle distributions, and the minority sessions show the compactness estate. additionally, it specializes in the difficult situations the place the aid areas of the bulk and minority sessions overlap. the writer has constructed powerful algorithms with theoretical promises and stable empirical effects for the similar thoughts, and those are defined intimately. The booklet is acceptable for researchers within the quarter of man-made intelligence, particularly desktop studying and information mining.
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Extra info for Analysis of Rare Categories
9. On the Ecoli data set, to discover all the classes, MALICE needs 36 label requests, Interleave needs 41 label requests on average, RS needs 43 label requests on average, Kernel needs 78 label requests, and SEDER only needs 20 label requests; on the Glass data set, to discover all the classes, MALICE needs 18 label requests, Interleave needs 24 label requests on average, RS needs 31 label requests on average, Kernel needs 102 label requests, and SEDER needs 22 label requests. Therefore, on these ‘moderately’ skewed data sets, the performance of SEDER is better than or comparable with MALICE, which requires more prior information than SEDER including the number of classes in the data set and the proportions of different classes.
In the separable case where the support regions of the majority and the minority classes do not overlap, we can use other methods to detect the minority classes, such as the one proposed in [PM04]. 1 Rare Category Detection with Priors for Data with Features 25 Algorithm 2 Active Learning for Initial Class Exploration (ALICE) Input: S, p2 , . . , pm 1: Initialize all the minority classes as undiscovered. 2: for c = 2 : m do 3: Let Kc = npc , where n is the number of examples. 4: For each example, calculate the distance between this example and its Kcth nearest neighbor.
P}. In SEDER, we set the vector of sufﬁcient statistics to be t(x) = [(x1 )2 , . . , (xd )2 ]T 7 . If we estimate the parameters according to Theorem 3, different parameters will be coupled due to the normalizing parameter β0 . Let β1j be the j th component of the vector β1 . In order to de-couple the estimation of different β1j s, we make the following changes. 7) j where β0i implies the dependence of β0j on xji . In this way, the marginal distribution of the j th feature is gβj (xj ) = 7 x1 ··· xj−1 xj+1 ··· xd gβ (x)dxd · · · dxj+1 dxj−1 · · · dx1 Note that the following analysis also applies to other forms of the sufﬁcient statistics, such as t(x) = [x1 , .