Robust weighted kernel logistic regression in imbalanced and rare events data

Maalouf, Maher; Trafali̇s, Theodore B.

doi:10.1016/j.csda.2010.06.014

Cited by 118 publications

(72 citation statements)

References 64 publications

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“…Rare events have occurrence frequencies that are low (Maalouf and Trafalis, 2011), with the number of events in the dataset dozens to thousands times smaller than the number of non-events. King and Zeng (2001a, b) have shown that rare events are difficult to predict as the standard application of logistic regression techniques can sharply underestimate the probability for rare events.…”

Section: Introductionmentioning

confidence: 99%

Logistic regression applied to natural hazards: rare event logistic regression with replications

Guns

Vanacker

2012

Nat. Hazards Earth Syst. Sci.

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Abstract. Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, socalled rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

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Section: Introductionmentioning

confidence: 99%

Logistic regression applied to natural hazards: rare event logistic regression with replications

Guns

Vanacker

2012

Nat. Hazards Earth Syst. Sci.

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“…Adding a dither to a signal is an established technique for improving the accuracy of a subsequent quantizer output [29]. This takes advantage of bias reduction while increasing the linear separability as well as the classification accuracy [15,26]. Also, the amplitude of the forcing function increases and the oscillations become random after dithering.…”

Section: The Effect Of Ditheringmentioning

confidence: 99%

Principal differential analysis for detection of bilabial closure gestures from articulatory data

Sattar

Rudzicz

2016

Computer Speech & Language

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“…In addition, Maalouf et al proposes a robust weighted kernel logistic regression. It can correct the bias of logistic regression in imbalanced classification [15].…”

Section: Imbalanced Classificationmentioning

confidence: 99%

Sarcasm Detection in Social Media Based on Imbalanced Classification

Liu

Chen

et al. 2014

Web-Age Information Management

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Abstract. Sarcasm is a pervasive linguistic phenomenon in online documents that express subjective and deeply-felt opinions. Detection of sarcasm is of great importance and beneficial to many NLP applications, such as sentiment analysis, opinion mining and advertising. Current studies consider automatic sarcasm detection as a simple text classification problem. They do not use explicit features to detect sarcasm and ignore the imbalance between sarcastic and non-sarcastic samples in real applications. In this paper, we first explore the characteristics of both English and Chinese sarcastic sentences and introduce a set of features specifically for detecting sarcasm in social media. Then, we propose a novel multi-strategy ensemble learning approach(MSELA) to handle the imbalance problem. We evaluate our proposed model on English and Chinese data sets. Experimental results show that our ensemble approach outperforms the state-of-the-art sarcasm detection approaches and popular imbalanced classification methods.

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Robust weighted kernel logistic regression in imbalanced and rare events data

Abstract: Benchmark datasets optimal parameter values with balanced training sets.. 6.3 Benchmark datasets bootstrap accuracy (%) comparison using balanced training sets. Bold accuracy values indicate the highest accuracy reached

Cited by 118 publications

References 64 publications

Logistic regression applied to natural hazards: rare event logistic regression with replications

Logistic regression applied to natural hazards: rare event logistic regression with replications

Principal differential analysis for detection of bilabial closure gestures from articulatory data

Sarcasm Detection in Social Media Based on Imbalanced Classification

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