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Auto Associative Extreme Learning Machine Based Hybrids for Data Imputation

Auto Associative Extreme Learning Machine Based Hybrids for Data Imputation

Chandan Gautam, Vadlamani Ravi
Copyright: © 2020 |Pages: 27
ISBN13: 9781799824602|ISBN10: 1799824608|EISBN13: 9781799824619
DOI: 10.4018/978-1-7998-2460-2.ch045
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MLA

Gautam, Chandan, and Vadlamani Ravi. "Auto Associative Extreme Learning Machine Based Hybrids for Data Imputation." Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 867-893. https://doi.org/10.4018/978-1-7998-2460-2.ch045

APA

Gautam, C. & Ravi, V. (2020). Auto Associative Extreme Learning Machine Based Hybrids for Data Imputation. In I. Management Association (Ed.), Cognitive Analytics: Concepts, Methodologies, Tools, and Applications (pp. 867-893). IGI Global. https://doi.org/10.4018/978-1-7998-2460-2.ch045

Chicago

Gautam, Chandan, and Vadlamani Ravi. "Auto Associative Extreme Learning Machine Based Hybrids for Data Imputation." In Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 867-893. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2460-2.ch045

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Abstract

This chapter presents three novel hybrid techniques for data imputation viz., (1) Auto-associative Extreme Learning Machine (AAELM) with Principal Component Analysis (PCA) (PCA-AAELM), (2) Gray system theory (GST) + AAELM with PCA (Gray+PCA-AAELM), (3) AAELM with Evolving Clustering Method (ECM) (ECM-AAELM). Our prime concern is to remove the randomness in AAELM caused by the random weights with the help of ECM and PCA. This chapter also proposes local learning by invoking ECM as a preprocessor for AAELM. The proposed methods are tested on several regression, classification and bank datasets using 10 fold cross validation. The results, in terms of Mean Absolute Percentage Error (MAPE,) are compared with that of K-Means+Multilayer perceptron (MLP) imputation (Ankaiah & Ravi, 2011), K-Medoids+MLP, K-Means+GRNN, K-Medoids+GRNN (Nishanth & Ravi, 2013) PSO_Covariance imputation (Krishna & Ravi, 2013) and ECM-Imputation (Gautam & Ravi, 2014). It is concluded that the proposed methods achieved better imputation in most of the datasets as evidenced by the Wilcoxon signed rank test.

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