Model for measuring accuracies of majority voting of ensemble classifier with COB and genetic algorithm

Site, Sarwesh; Mishra, Sadhna K.

doi:10.1109/icices.2013.6508317

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Ensemble learning is to get several classifiers (called basic learners) in the process of training samples, and then combine these basic learners in a certain way, so that multiple basic learners work together to solve a learning task. For the combination of basic learners, the traditional methods are generally as follows: majority voting method [9], weight voting method [10], hierarchical combination method [11], etc. These integrated learning algorithms can effectively solve all kinds of classification and prediction problems, but they still have shortcomings.…”

Section: Related Workmentioning

confidence: 99%

Household Power Consumption Prediction Method Based on Selective Ensemble Learning

2020

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In the context power big data, the household power consumption data on the user side has the characteristics of large quantity, wide distribution and many types. Ensemble learning is very excellent in the analysis and mining of power data with large amount of data, strong timeliness and many influencing factors. Based on the data of household power consumption, this paper analyzes and predicts the power consumption of some users in a city by using selective ensemble learning technology and combining with meteorological factors. In this paper, K-means algorithm is used to cluster the household power consumption, and then the clustering results are combined with the meteorological information. In the stage of power consumption prediction, a Filter Iterative Optimization Ensemble Strategy (FIOES) is proposed to selectively ensemble the basic learners and get the final prediction model. Experimental results show that the FIOES algorithm has better performance in time cost and prediction accuracy than the traditional ensemble learning algorithm. INDEX TERMS Power big data, ensemble learning, power consumption, iterative optimization.

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Section: Related Workmentioning

confidence: 99%