An error correction prediction model based on three-way decision and ensemble learning

Huang, Xin; Zhan, Jianming; Ding, Weiping; Pedrycz, Witold

doi:10.1016/j.ijar.2022.04.002

Cited by 22 publications

(2 citation statements)

References 60 publications

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“…Huang et al proposed an EC method for BPNN based on the three-way decision and integration model. 19) The training data were divided into three categories according to the prediction error of BPNN on the training data. Then, three algorithms were used to build models for each data type to predict the BPNN error and correct the predicted value of the BPNN model.…”

Section: Steel In Ladle Furnacementioning

confidence: 99%

An Error Correction Method Based on CBR for End Temperature Prediction of Molten Steel in Ladle Furnace

He,

Song,

Guo

et al. 2024

ISIJ Int.

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Accurately predicting the end temperature of molten steel is significant for controlling ladle furnace (LF) refining. This paper proposes an error correction method called EC-CBR based on case-based reasoning (CBR) to reduce errors in the prediction models caused by discrepancies between actual production data and training data. The proposed method combines the incremental learning advantage of CBR with the ability of other models to fit nonlinear relations. First, a prediction model is established, and historical heats similar to the new heat are retrieved by CBR. Then, the model error of the new heat is calculated by employing the errors of similar heats. The prediction result is calculated by subtracting the error from the predicted value. Testing and comparison are conducted on the models (support vector regression, backpropagation neural network, extreme learning machine and mechanism model) and general CBR using actual production data. Results show that the EC-CBR is effective for both data-driven and mechanism models, with an increase of approximately 5% in hit rate within the range of ±5℃ for data-driven models and an increase of 21.73% for mechanism model. Moreover, the corrected data-driven models show higher accuracy than the general CBR, further proving the effectiveness of the proposed method.

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Section: Steel In Ladle Furnacementioning

confidence: 99%

An Error Correction Method Based on CBR for End Temperature Prediction of Molten Steel in Ladle Furnace

He,

Song,

Guo

et al. 2024

ISIJ Int.

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“…Three-way decisions [17], also known as trisecting-acting-outcome (TAO) model [18][19][20], is an emerging decision theory for problem solving with uncertainty [21][22][23]. It originates from the semantic explanations for three regions induced by rough sets and has become an emerging theory in the soft computing community [24][25][26][27]. The sequential three steps are trisecting, acting and outcome characterizing the routines on uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy-Based Three-Way Label Enhancement for Multi-Label Classification

2022

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Multi-label classification deals with the determination of instance-label associations for unseen instances. Although many margin-based approaches are delicately developed, the uncertainty classifications for those with smaller separation margins remain unsolved. The intuitionistic fuzzy set is an effective tool to characterize the concept of uncertainty, yet it has not been examined for multi-label cases. This paper proposed a novel model called intuitionistic fuzzy three-way label enhancement (IFTWLE) for multi-label classification. The IFTWLE combines label enhancement with an intuitionistic fuzzy set under the framework of three-way decisions. For unseen instances, we generated the pseudo-label for label uncertainty evaluation from a logical label-based model. An intuitionistic fuzzy set-based instance selection principle seamlessly bridges logical label learning and numerical label learning. The principle is hierarchically developed. At the label level, membership and non-membership functions are pair-wisely defined to measure the local uncertainty and generate candidate uncertain instances. After upgrading to the instance level, we select instances from the candidates for label enhancement, whereas they remained unchanged for the remaining. To the best of our knowledge, this is the first attempt to combine logical label learning with numerical label learning into a unified framework for minimizing classification uncertainty. Extensive experiments demonstrate that, with the selectively reconstructed label importance, IFTWLE achieves statistically superior over the state-of-the-art multi-label classification algorithms in terms of classification accuracy. The computational complexity of this algorithm is On2mk, where n, m, and k denote the unseen instances count, label count, and average label-specific feature size, respectively.

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A novel prediction approach based on three-way decision for cloud datacenters

Liu

Jiang

2023

Appl Intell

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An error correction prediction model based on three-way decision and ensemble learning

Cited by 22 publications

References 60 publications

An Error Correction Method Based on CBR for End Temperature Prediction of Molten Steel in Ladle Furnace

An Error Correction Method Based on CBR for End Temperature Prediction of Molten Steel in Ladle Furnace

Intuitionistic Fuzzy-Based Three-Way Label Enhancement for Multi-Label Classification

A novel prediction approach based on three-way decision for cloud datacenters

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