Root-quatric mixture of experts for complex classification problems

Abbasi, Elham; Shiri, Mohammad Ebrahim; Ghatee, Mehdi

doi:10.1016/j.eswa.2016.01.040

Cited by 10 publications

(2 citation statements)

References 41 publications

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“…However, there is no control over the bias-variance tradeoff. Combinations of NCL and ME implicit approaches exist [1,12]. These methods integrate an error function correlation penalty term to encourage different classifiers (NCL), through a divide and conquer approach (ME), to learn using different parts of the training data.…”

Section: Classificationmentioning

confidence: 99%

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Towards Sustainable Dairy Management - A Machine Learning Enhanced Method for Estrus Detection

Fauvel

Masson

Fromont

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Our research tackles the challenge of milk production resource use efficiency in dairy farms with machine learning methods. Reproduction is a key factor for dairy farm performance since cows milk production begin with the birth of a calf. Therefore, detecting estrus, the only period when the cow is susceptible to pregnancy, is crucial for farm efficiency. Our goal is to enhance estrus detection (performance, interpretability), especially on the currently undetected silent estrus (35% of total estrus), and allow farmers to rely on automatic estrus detection solutions based on affordable data (activity, temperature). In this paper, we first propose a novel approach with real-world data analysis to address both behavioral and silent estrus detection through machine learning methods. Second, we present LCE, a local cascade based algorithm that significantly outperforms a typical commercial solution for estrus detection, driven by its ability to detect silent estrus. Then, our study reveals the pivotal role of activity sensors deployment in estrus detection. Finally, we propose an approach relying on global and local (behavioral versus silent) algorithm interpretability (SHAP) to reduce the mistrust in estrus detection solutions. CCS CONCEPTS• Computing methodologies → Machine learning; • Applied computing → Agriculture.

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Section: Classificationmentioning

confidence: 99%

“…Then, the overfit generated by the decision tree divide and conquer bias reduction approach is mitigated by the use of bagging. Bagging provides variance reduction by creating multiple decision trees from different subsamples of the original dataset (random sampling with replacement, see 1 . .…”

Section: Lce: Local Cascade Ensemblementioning

confidence: 99%