Abstract
This paper presents an integrated approach by considering chemical reaction optimization (CRO) and functional link artificial neural networks (FLANNs) for building a classifier from the dataset with missing value, inconsistent records, and noisy instances. Here, imputation is carried out based on the known value of two nearest neighbors to address dataset plagued with missing values. The probabilistic approach is used to remove the inconsistency from either of the datasets like original or imputed. The resulting dataset is then given as an input to boosted instance selection approach for selection of relevant instances to reduce the size of the dataset without loss of generality and compromising classification accuracy. Finally, the transformed dataset (i.e., from non-imputed and inconsistent dataset to imputed and consistent dataset) is used for developing a classifier based on CRO trained FLANN. The method is evaluated extensively through a few bench-mark datasets obtained from University of California, Irvine (UCI) repository. The experimental results confirm that our preprocessing tasks along with integrated approach can be a promising alternative tool for mitigating missing value, inconsistent records, and noisy instances.
| Original language | English |
|---|---|
| Article number | 1950013 |
| Journal | International Journal on Artificial Intelligence Tools |
| Volume | 28 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2019 May 1 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
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An Integrated CRO and FLANN Based Classifier for a Non-Imputed and Inconsistent Dataset. / Dash, Ch Sanjeev Kumar; Behera, Ajit Kumar; Nayak, Sarat Chandra; Dehuri, Satchidananda; Cho, Sung Bae.
In: International Journal on Artificial Intelligence Tools, Vol. 28, No. 3, 1950013, 01.05.2019.Research output: Contribution to journal › Article
TY - JOUR
T1 - An Integrated CRO and FLANN Based Classifier for a Non-Imputed and Inconsistent Dataset
AU - Dash, Ch Sanjeev Kumar
AU - Behera, Ajit Kumar
AU - Nayak, Sarat Chandra
AU - Dehuri, Satchidananda
AU - Cho, Sung Bae
PY - 2019/5/1
Y1 - 2019/5/1
N2 - This paper presents an integrated approach by considering chemical reaction optimization (CRO) and functional link artificial neural networks (FLANNs) for building a classifier from the dataset with missing value, inconsistent records, and noisy instances. Here, imputation is carried out based on the known value of two nearest neighbors to address dataset plagued with missing values. The probabilistic approach is used to remove the inconsistency from either of the datasets like original or imputed. The resulting dataset is then given as an input to boosted instance selection approach for selection of relevant instances to reduce the size of the dataset without loss of generality and compromising classification accuracy. Finally, the transformed dataset (i.e., from non-imputed and inconsistent dataset to imputed and consistent dataset) is used for developing a classifier based on CRO trained FLANN. The method is evaluated extensively through a few bench-mark datasets obtained from University of California, Irvine (UCI) repository. The experimental results confirm that our preprocessing tasks along with integrated approach can be a promising alternative tool for mitigating missing value, inconsistent records, and noisy instances.
AB - This paper presents an integrated approach by considering chemical reaction optimization (CRO) and functional link artificial neural networks (FLANNs) for building a classifier from the dataset with missing value, inconsistent records, and noisy instances. Here, imputation is carried out based on the known value of two nearest neighbors to address dataset plagued with missing values. The probabilistic approach is used to remove the inconsistency from either of the datasets like original or imputed. The resulting dataset is then given as an input to boosted instance selection approach for selection of relevant instances to reduce the size of the dataset without loss of generality and compromising classification accuracy. Finally, the transformed dataset (i.e., from non-imputed and inconsistent dataset to imputed and consistent dataset) is used for developing a classifier based on CRO trained FLANN. The method is evaluated extensively through a few bench-mark datasets obtained from University of California, Irvine (UCI) repository. The experimental results confirm that our preprocessing tasks along with integrated approach can be a promising alternative tool for mitigating missing value, inconsistent records, and noisy instances.
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UR - http://www.scopus.com/inward/citedby.url?scp=85066628148&partnerID=8YFLogxK
U2 - 10.1142/S0218213019500131
DO - 10.1142/S0218213019500131
M3 - Article
AN - SCOPUS:85066628148
VL - 28
JO - International Journal on Artificial Intelligence Tools
JF - International Journal on Artificial Intelligence Tools
SN - 0218-2130
IS - 3
M1 - 1950013
ER -