On Adjustment Functions for Weight-Adjusted Voting-Based Ensembles of Classifiers

Hsu, Kuo-Wei

doi:10.4304/jcp.9.7.1547-1552

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Gini Impurity(S) = 𝟏𝟏 − ∑ (𝒑𝒑𝒊𝒊) 𝟐𝟐 𝒏𝒏 𝒊𝒊=𝟏𝟏 (13) Gini impurity is a measure of how likely it is that a randomly chosen data point from S will be misclassified if we assign it to the most common class at the node.…”

Section: Entropy and Gini Impurity In Decision Treesmentioning

confidence: 99%

“…Overall, the review emphasizes the effectiveness of machine learning approaches, especially when enhanced by ensemble techniques, in predicting precipitation." The inspiration for incorporating the Voting Regressor in our ensemble method stems from an in-depth study on Voting Classifiers [13]. This research paper emphasized the effectiveness of ensemble methods in enhancing prediction accuracy by aggregating multiple base models.…”

Section: Introductionmentioning

confidence: 99%

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Prognostication of Weather Patterns using Meteorological Data and ML Techniques

Mathur,

Kumar,

Choudhury

2024

EAI Endorsed Trans Energy Web

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In the field of modern weather prediction, the accurate classification is essential, impacting critical sectors such as agriculture, aviation, and water resource management. This research presents a weather forecasting model employing two influential classifiers random forest and technique based on gradient boosting, both implemented using the Scikit-learn library. Evaluation is based on key metrics including F1 score, accuracy, recall, and precision, with Gradient Boosting emerging as the superior choice for precipitation prediction. The study examines the performance of Random Forest Regression, Gradient Boosting Regression, and Radial Basis Function Neural Network in forecasting precipitation, drawing on prior research that demonstrated the superiority of the Random Forest algorithm in terms of accuracy and speed. Ensemble methods, particularly the Voting Classifier, a fusion of Random Forest and Gradient Boosting, outperform individual models, offering a promising avenue for advancing weather classification.

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Section: Entropy and Gini Impurity In Decision Treesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prognostication of Weather Patterns using Meteorological Data and ML Techniques

Mathur,

Kumar,

Choudhury

2024

EAI Endorsed Trans Energy Web

View full text Add to dashboard Cite

show abstract

“…The identification of sub goal states or nodes as cluster heads has been described in [24]. The calculation of weights associated with classifiers has been described in [25][26][27][28][29][30]. The computation of similarity and complement measures associated with classifiers has been described in [14,31].…”

Section: Introductionmentioning

confidence: 99%

Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach

Mukherjee

Bandyopadhyay

2017

J Big Data

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This paper proposes a reinforcement learning based message transfer model for transferring news report messages through a selected path in a trusted provenance network with the objective of maximizing the reward values based on trust or importance based and network congestion or utility based cost measures. The reward values are calculated along a dynamically defined policy path connecting start topic or event node to a goal topic or event or issue nodes for incrementally defined time windows for a given network congestion situation. A hierarchy of agents of trusted roles is used to accomplish the sub-goals associated with sub-story or subtopic in the provenance structure where an agent role may assume the semantic role of the associated subtopic. The twitted news story thread or plan of events is defined in this work from the starting topic or event node to the goal topic or event node for incrementally defined intervals of time. The graphs are clustered into subtopic and these sub-goals or sub topic nodes of a topic node at every level of granularity are associated with cluster of news reports which describe activities associated with sub-goal or sub-topic events. Such cluster of nodes may also represent drilled down sequence of sub-events describing a sub-topic or sub-goal node. The policy path in a topic or story graph model is defined by applying reinforcement learning principles on dynamically defined event models associated with evolution of topic definition observed from incrementally acquired samples of input training data spanning multiple time windows. We provide a methodology for unifying similar provenance graph models for adapting and averaging the policy path classifiers associated with individual models to produce a reduced set of unified models derived during training. A minimum set cover of classifiers is identified for the models and a clustering procedure of the models is suggested based on these classifiers. Other database clustering methods have also been suggested as alternatives for clustering these models. A collection of unified models are identified from the models identified within a cluster and the policy path classifiers associated with these models provide the story or topic descriptions destined to goal topic or event nodes characterizing these models within a cluster.

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Weight Modification Method for Performance Evaluation Model of Computing System Based on Bayes' Theorem

Da-liang

Lan

2018

Proceedings of the 2018 2nd International Conference on Management Engineering, Software Engineering and Service Sciences

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On Adjustment Functions for Weight-Adjusted Voting-Based Ensembles of Classifiers

Cited by 3 publications

References 25 publications

Prognostication of Weather Patterns using Meteorological Data and ML Techniques

Prognostication of Weather Patterns using Meteorological Data and ML Techniques

Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach

Weight Modification Method for Performance Evaluation Model of Computing System Based on Bayes' Theorem

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