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- W2896896766 abstract "In this work, we first proposed Quantile Regression Random Forest (QRRF) to impute missing data values. Further, we developed two new 2-stage hybrid architectures viz. (i) K-Means + QRRF and (ii) K-Medoids + QRRF. In the first stage, missing values are replaced by the components of cluster centers obtained by K-Medoids or K-Means algorithm. In the second stage, QRRF is used to fine tune the imputation performed in the first stage. We compared the performance of proposed techniques with that of various extant techniques. We experimented with twelve datasets including four benchmark regression datasets, four standard classification datasets, three bankruptcy prediction, and credit scoring datasets. We observed that the QRRF, K-Means+QRRF and K-Medoids+QRRF outperformed, in turns, all other tested alternatives in terms of Mean Absolute Percentage Error (MAPE) values under 10-fold cross validation. We also conducted Wilcoxon signed-rank test between the proposed techniques and five other imputation techniques." @default.
- W2896896766 created "2018-10-26" @default.
- W2896896766 creator A5073505371 @default.
- W2896896766 creator A5077364572 @default.
- W2896896766 date "2018-07-01" @default.
- W2896896766 modified "2023-09-28" @default.
- W2896896766 title "Quantile Regression Random Forest Hybrids Based Data Imputation" @default.
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- W2896896766 doi "https://doi.org/10.1109/icci-cc.2018.8482040" @default.
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