Weighted Voting Based Ensemble Classification with Hyper-parameter Optimization

Gökalp, Osman; Taşçı, Erdal

doi:10.1109/asyu48272.2019.8946373

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…ML's application extends to improving plant agronomic traits through the integration of large omics data (Isewon et al, 2022). Studies by Farooq et al (2022), Isewon et al (2022), andSilva et al (2019) highlight the superiority of ML methods, particularly decision tree-based ensemble models (Gokalp and Tasci, 2019), in genomic prediction and integrative analysis of plant omics data. ML's potential in deciphering complex interactions in plant molecular biology, including pathogen effector genes and plant immunity, is underscored (Silva et al, 2019).…”

Section: Validation Strategiesmentioning

confidence: 99%

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

Murmu,

Sinha,

Chaurasia

et al. 2024

Front. Plant Sci.

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Plants intricately deploy defense systems to counter diverse biotic and abiotic stresses. Omics technologies, spanning genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the exploration of plant defense mechanisms, unraveling molecular intricacies in response to various stressors. However, the complexity and scale of omics data necessitate sophisticated analytical tools for meaningful insights. This review delves into the application of artificial intelligence algorithms, particularly machine learning and deep learning, as promising approaches for deciphering complex omics data in plant defense research. The overview encompasses key omics techniques and addresses the challenges and limitations inherent in current AI-assisted omics approaches. Moreover, it contemplates potential future directions in this dynamic field. In summary, AI-assisted omics techniques present a robust toolkit, enabling a profound understanding of the molecular foundations of plant defense and paving the way for more effective crop protection strategies amidst climate change and emerging diseases.

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Section: Validation Strategiesmentioning

confidence: 99%

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

Murmu,

Sinha,

Chaurasia

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

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“…Multiple learning algorithms make the classification model more robust. Methods like this increase efficiency but can be biased as performance heavily depends on weights in a weighted voting (Gokalp and Tasci 2019 ). These can involve a combination of any of the classifiers described above.…”

Section: Traditional Classifiersmentioning

confidence: 99%

Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence

Ardern

Chakraborty

Lenk

et al. 2023

FEMS Microbiology Reviews

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Annotating protein sequences according to their biological functions is one of the key steps in understanding microbial diversity, metabolic potentials and evolutionary histories. However, even in the best-studied prokaryotic genomes, not all proteins can be characterized by classical in vivo, in vitro, and/or in silico methods—a challenge rapidly growing alongside the advent of Next Generation Sequencing technologies and their enormous extension of ‘omics’ data in public databases. These so-called hypothetical proteins (HPs) represent a huge knowledge gap and hidden potential for biotechnological applications. Opportunities for leveraging the available ‘Big Data’ have recently proliferated with the use of artificial intelligence (AI). Here we review the aims and methods of protein annotation and explain the different principles behind machine and deep learning algorithms including recent research examples, in order to assist both biologists wishing to apply AI tools in developing comprehensive genome annotations and computer scientists who want to contribute to this leading edge of biological research.

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“…It has been observed over time that ensemble methods, if not properly checked, might not ensure that the best-performing set of weights are used as a final model. So, our proposed method performed weighted average ensemble [8] as one of the ways of achieving a model ensemble in neural networks like voting [9] and stacking [10] and snapshot or checkpoint [11], among others, in a unique way. Here, instead of allowing equal contribution of all the models to the final prediction model, contributions were dependent on the level of trust and estimated performance, to ensure the performance of poorly performed models do not affect the overall forecast result.…”

Section: The Network Architecturementioning

confidence: 99%

Smart power consumption forecast model with optimized weighted average ensemble

Ndife

Rakwichian

Muneesawang

et al. 2022

IJ-AI

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Smart power forecasting enables energy conservation and resource planning. Power estimation through previous utility bills is being replaced with machine intelligence. In this paper, a neural network architecture for demand side power consumption forecasting, called SGtechNet, is proposed. The forecast model applies ConvLSTM-encoder-decoder algorithm designed to enhance the quality of spatial encodings in the input feature to make a 7-day forecast. A weighted average ensemble approach was used, where multiple models were trained but only allow each model’s contribution to the prediction to be weighted proportionally to their level of trust and estimated performance. This model is most suitable for low-powered devices with low processing and storage capabilities like smartphones, tablets and iPads. The power consumption comparison between a manually operated home and a smart home was investigated and the model’s performance was tested on a time-domain household power consumption dataset and further validated using a real time load profile collated from the School of Renewable Energy and Smart Grid Technology, Naresuan University Smart Office. An improved root mean square error (RMSE) of 358 kwh was achieved when validated with holdout validation data from the automated office. Overall performance error, forecast and computational time showed a significant improvement over published research efforts identified in a literature review.

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Weighted Voting Based Ensemble Classification with Hyper-parameter Optimization

Cited by 7 publications

References 7 publications

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence

Smart power consumption forecast model with optimized weighted average ensemble

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