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Multi-stack ensemble for job recommendation
Proceedings of the Recommender Systems Challenge, 2016This paper describes the approach that team PumpkinPie adopted in the 2016 Recsys Challenge. The task of the competition organized by XING is to predict which job postings the user has interacted with. The team's approach mainly consists in generating a set of models using different techniques, and then combining them in a multi-stack ensemble.
CARPI, TOMMASO +6 more
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PreTP-Stack: Prediction of Therapeutic Peptide Based on the Stacked Ensemble Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023Therapeutic peptide prediction is critical for drug development and therapeutic therapy. Researchers have developed several computational methods to identify different therapeutic peptide types. However, most computational methods focus on identifying the specific type of therapeutic peptides and fail to accurately predict all types of therapeutic ...
Ke Yan +5 more
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Fast adaptive stacking of ensembles
Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016This paper presents a new ensemble method for learning from non-stationary data streams. In these situations, massive data are constantly generated at high speed and their target function can change over time. The proposed method, named Fast Adaptive Stacking of Ensembles (FASE), uses a meta-classifier to combine the predictions from the base ...
Isvani Frías-Blanco +3 more
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Distracted Driver Detection using Stacking Ensemble
2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS), 2020Distracted driving is one of the primary causes of car crashes. While driving the vehicle, drivers frequently perform secondary activities that distract driving. A decrease in driver distraction is a critical aspect of the smart transportation system.
Ketan Ramesh Dhakate, Ratnakar Dash
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Stacking Ensemble-based Automatic Web Page Classification
2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT), 2021The World Wide Web is expanding at an exponential rate resulting in a vast amount of information. Therefore, search engines need an automated web page classification system to avoid manual classification. Web page classification plays a vital role in deciding the fate of a Focused web crawler by moving it towards the relevant section of the web ...
Deeksha Deeksha +5 more
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Improving Fast Adaptive Stacking of Ensembles
2019 International Joint Conference on Neural Networks (IJCNN), 2019The treatment of large data streams in the presence of concept drifts is one of the main challenges in the fields of machine learning and data mining. This article presents two ensemble algorithms designed to quickly adapt to concept drifts, both abrupt and gradual.
Laura M. P. Marino +3 more
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Negation Handling using Stacking Ensemble Method
2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), 2017Polarity shift is the major problem in the Bag-of-words model. Polarity shifting occurs when the polarity of the sentence is different from the polarity expressed by the sum of the content words in the sentence. Polarity shift reverses the sentiment polarity of the text.
Sayali Zirpe, Bela Joglekar
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Reranking for Stacking Ensemble Learning
2010Ensemble learning refers to the methods that combine multiple models to improve the performance. Ensemble methods, such as stacking, have been intensively studied, and can bring slight performance improvement. However, there is no guarantee that a stacking algorithm outperforms all base classifiers.
Buzhou Tang +3 more
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Ensemble deep learning: A review
Engineering Applications of Artificial Intelligence, 2022Minghui Hu +2 more
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