Active Sampling for Multiple Output Identification

Fine, Shai; Mansour, Yishay

doi:10.1007/11776420_45

Cited by 14 publications

(17 citation statements)

References 17 publications

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“…An example of the underlying distribution where these assumptions are satisfied is shown in Figure 1. Note that these assumptions are much more realistic than the separable/nearseparable assumption assumed in [8] [14]. Based on our assumptions, abrupt changes in local density indicate the presence of rare classes.…”

Section: Semiparametric Density Estimation For Rarementioning

confidence: 85%

“…For example, the method based on mixture models proposed in [14] is among the first attempts in this direction; in [8], the authors proposed a generic consistency algorithm, and proved upper bounds and lower bounds for this algorithm in some specific situations. Both of the two methods require that the support regions of the different classes be separable or near-separable to work well.…”

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confidence: 99%

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Prior-Free Rare Category Detection

Carbonell

2009

Proceedings of the 2009 SIAM International Conference on Data Mining

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Rare category detection is an open challenge in machine learning. It plays the central role in applications such as detecting new financial fraud patterns, detecting new network malware, and scientific discovery. In such cases rare categories are hidden among huge volumes of normal data and observations. In this paper, we propose a new method for rare category detection named SEDER, which requires no prior information about the data set. It implicitly performs semiparametric density estimation using specially designed exponentially families, and then picks the examples for labeling where the neighborhood density changes the most. SEDER can work in the cases where the data is not separable. Its unique feature over all existing methods lies in its prior-free nature, i.e. it does not require any prior information about the data set (e.g. the number of classes, the proportion of the different classes, etc.). Therefore, it is more suitable for real applications. Experimental results on both synthetic and real data sets demonstrate the superiority of SEDER.

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Section: Semiparametric Density Estimation For Rarementioning

confidence: 85%

mentioning

confidence: 99%

Prior-Free Rare Category Detection

Carbonell

2009

Proceedings of the 2009 SIAM International Conference on Data Mining

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“…However, in practice the user of a fuzzer does not usually care about the tests in a cluster, but only about finding at least one example from each set with no particular desire that it is a perfectly "representative" example. The core problem we address is therefore better considered as one of multiple output identification [8] or rare category detection [8,40], given that many faults will be found by a single test case out of thousands. This insight guides our decision to provide the first evaluation in terms of discovery curves (the most direct measure of fuzzer taming capability we know of) for this problem.…”

Section: Related Workmentioning

confidence: 99%

Taming compiler fuzzers

Yang

Groce

Zhang

et al. 2013

Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation

121

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Aggressive random testing tools ("fuzzers") are impressively effective at finding compiler bugs. For example, a single test-case generator has resulted in more than 1,700 bugs reported for a single JavaScript engine. However, fuzzers can be frustrating to use: they indiscriminately and repeatedly find bugs that may not be severe enough to fix right away. Currently, users filter out undesirable test cases using ad hoc methods such as disallowing problematic features in tests and grepping test results. This paper formulates and addresses the fuzzer taming problem: given a potentially large number of random test cases that trigger failures, order them such that diverse, interesting test cases are highly ranked. Our evaluation shows our ability to solve the fuzzer taming problem for 3,799 test cases triggering 46 bugs in a C compiler and 2,603 test cases triggering 28 bugs in a JavaScript engine.

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“…Fine et al [5] abstract the rare category detection problem as an output identification task in a learning model. The learning model has an unknown target function f which maps every input in χ to one of m output values.…”

Section: Related Workmentioning

confidence: 99%

Category detection using hierarchical mean shift

Vatturi

Wong

2009

Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine learning that can help address this issue using a "human-in-the-loop" approach. In this interactive setting, the algorithm asks the user to label a query data point under an existing category or declare the query data point to belong to a previously undiscovered category. The goal of category detection is to bring to the user's attention a representative data point from each category in the data in as few queries as possible. In a data set with imbalanced categories, the main challenge is in identifying the rare categories or anomalies; hence, the task is often referred to as rare category detection. We present a new approach to rare category detection based on hierarchical mean shift. In our approach, a hierarchy is created by repeatedly applying mean shift with an increasing bandwidth on the data. This hierarchy allows us to identify anomalies in the data set at different scales, which are then posed as queries to the user. The main advantage of this methodology over existing approaches is that it does not require any knowledge of the dataset properties such as the total number of categories or the prior probabilities of the categories. Results on real-world data sets show that our hierarchical mean shift approach performs consistently better than previous techniques.

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Active Sampling for Multiple Output Identification

Cited by 14 publications

References 17 publications

Prior-Free Rare Category Detection

Prior-Free Rare Category Detection

Taming compiler fuzzers

Category detection using hierarchical mean shift

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