Boost-wise pre-loaded mixture of experts for classification tasks

Ebrahimpour, Reza; Sadeghnejad, Naser; Arani, Seyed Ali Asghar Abbaszadeh; Mohammadi, Nima

doi:10.1007/s00521-012-0909-2

Cited by 6 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, implicit approaches combinations do not benefit from the improved generalization ability of explicitly creating different training sets by probabilistically changing the distribution of the original training data (bagging, boosting). A method combining the explicit boosting approach with implicit ME divide and conquer approach exists [13]. Nonetheless, the low bias distribution change of boosting does not ensure a bias-variance tradeoff.…”

Section: Classificationmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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.

show abstract

Section: Classificationmentioning

confidence: 99%