Jingrui He's Analysis of Rare Categories PDF

By Jingrui He

ISBN-10: 3642228127

ISBN-13: 9783642228124

In many real-world difficulties, infrequent different types (minority sessions) play crucial roles regardless of their severe shortage. the invention, characterization and prediction of infrequent different types of infrequent examples may well guard us from fraudulent or malicious habit, reduction clinical discovery, or even shop lives.

This ebook specializes in infrequent classification research, the place the bulk periods have soft distributions, and the minority sessions show the compactness estate. additionally, it makes a speciality of the demanding instances the place the help areas of the bulk and minority sessions overlap. the writer has constructed potent algorithms with theoretical promises and sturdy empirical effects for the comparable suggestions, and those are defined intimately. The publication is acceptable for researchers within the zone of synthetic intelligence, particularly desktop studying and information mining.

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11) where sk is the score of xk . We pick the examples with the largest scores for labeling until we find at least one example from each class. 2 Algorithm The intuition of SEDER is to select the examples with the maximum change in the local density for labeling by the oracle. 1, the scores of the examples measure the maximum change rate in the local density, and they do not take into account the fact that nearby examples tend to have the same class label. Therefore, if we ask the oracle to label all the examples with large scores, we may repeatedly select examples from the most distinctive rare class, rather than discovering all the rare classes.

2. f1 (x) is bounded and positive in Bc , c = 2, . . 4 rc Furthermore, for each minority class c, c = 2, . . , m, let rc2 = 1 ; (1+κc2 ) d and let OV ( r2c2 , rc ) be the volume of the overlapping region of two hyperballs: one is of radius rc ; the other one is of radius r2c2 , and its center is on the sphere of the previous one. To prove the performance of the proposed ALICE algorithm, we first have the following lemma. Lemma 2. For each minority class c, c = 2, . . , m, ∀ c , δc > 0, if n ≥ 3m−3 1 max{maxm c=2 2κ2 p2 log δ , maxm c=2 c1 c 1 2(1−2−d )2 p2c ability at least 1 − δ, m log 3m−3 δ , maxc=2 rc2 2 1 4 V ( rc2 )4 2 nci log 3m−3 δ }, then with prob- nc ≤ rc ≤ rc and | n − E( ni )| ≤ V (rc ), 1 ≤ j ≤ n.

5 4 3 2 1 0 −1 −3 −2 −1 0 1 2 3 (b) Examples selected by NNDB, denoted as green ‘x’s. 1: Synthetic data set 1. 4 31 32 3 Rare Category Detection 200 180 160 140 120 100 80 60 40 20 0 0 50 100 150 200 250 (a) Data set: there are 3000 examples from the majority class, denoted as blue dots; there are 138, 79, 118, and 206 examples from each minority class, denoted as red balls. 200 180 160 140 120 100 80 60 40 20 0 0 50 100 150 200 (b) Examples selected by MALICE, denoted as green ‘x’s. 2: Synthetic data set 2.

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