An Integrated CRO and FLANN Based Classifier for a Non-Imputed and Inconsistent Dataset

2019 ◽  
Vol 28 (03) ◽  
pp. 1950013 ◽  
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Ajit Kumar Behera ◽  
Sarat Chandra Nayak ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Missing Values ◽  
Probabilistic Approach ◽  
Integrated Approach ◽  
Bench Mark ◽  
Chemical Reaction Optimization ◽  
Functional Link ◽  
Missing Value ◽  
Promising Alternative ◽  
Reaction Optimization ◽  
Selection Of

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.

Sliding Mode Control Based on Chemical Reaction Optimization and Radial Basis Functional Link Net for De-Icing Robot Manipulator

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Thuy Van Tran ◽  
YaoNan Wang ◽  
HungLinh Ao ◽  
Tung Khac Truong
Keyword(s):  
Sliding Mode Control ◽  
Chemical Reaction ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Stability And Convergence ◽  
Chemical Reaction Optimization ◽  
Functional Link ◽  
Mode Control ◽  
Reaction Optimization ◽  
Radial Basis

In this paper, a sliding mode control (SMC) system based on combining chemical reaction optimization (CRO) algorithm with radial basis functional link net (RBFLN) for an n-link robot manipulator is proposed to achieve the high-precision position tracking. In the proposed scheme, a three-layer RBFLN with powerful approximation ability is employed to approximate the uncertainties, such as parameter variations, friction forces, and external disturbances, and to eliminate chattering phenomenon of the SMC. In order to achieve the expected performance in the initial phase as well as the improved convergence rate, the RBFLN parameters need to be optimized in advance. Therefore, the initial parameters of the RBFLN are optimized offline by CRO algorithm instead of random selection. Furthermore, the RBFLN weights are determined online according to adaptive tuning laws in the sense of a projection algorithm and the Lyapunov stability theorem to guarantee the stability and convergence of the system. The simulation results of three-link de-icing robot manipulator (DIRM) are provided to verify the robustness and effectiveness of the proposed methodology.

Feature Selection Algorithm for High-dimensional Biomedical Data Using Information Gain and Improved Chemical Reaction Optimization

2021 ◽  
Vol 15 (8) ◽  
pp. 912-926
Author(s):  
Ge Zhang ◽  
Pan Yu ◽  
Jianlin Wang ◽  
Chaokun Yan
Keyword(s):  
Feature Selection ◽  
Chemical Reaction ◽  
Information Gain ◽  
Feature Selection Method ◽  
Search Space ◽  
Neighborhood Search ◽  
Biomedical Data ◽  
Chemical Reaction Optimization ◽  
Search Mechanism ◽  
Reaction Optimization

Background: There have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. However, these datasets usually involve thousands of features and include much irrelevant or redundant information, which leads to confusion during diagnosis. Feature selection is a solution that consists of finding the optimal subset, which is known to be an NP problem because of the large search space. Objective: For the issue, this paper proposes a hybrid feature selection method based on an improved chemical reaction optimization algorithm (ICRO) and an information gain (IG) approach, which called IGICRO. Methods: IG is adopted to obtain some important features. The neighborhood search mechanism is combined with ICRO to increase the diversity of the population and improve the capacity of local search. Results: Experimental results of eight public available data sets demonstrate that our proposed approach outperforms original CRO and other state-of-the-art approaches.

scholarly journals Kernel weighted least square approach for imputing missing values of metabolomics data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nishith Kumar ◽  
Md. Aminul Hoque ◽  
Masahiro Sugimoto
Keyword(s):  
Missing Data ◽  
Large Scale ◽  
Missing Values ◽  
Kernel Weight ◽  
Least Square ◽  
Data Matrix ◽  
Data Imputation ◽  
Metabolomics Data ◽  
Missing Value ◽  
Missing Data Imputation

AbstractMass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing cells in the data matrix as well as outliers that originate for several reasons, including technical and biological sources. Although several missing data imputation techniques are described in the literature, all conventional existing techniques only solve the missing value problems. They do not relieve the problems of outliers. Therefore, outliers in the dataset decrease the accuracy of the imputation. We developed a new kernel weight function-based proposed missing data imputation technique that resolves the problems of missing values and outliers. We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both artificially generated data and experimentally measured data analysis in both the absence and presence of different rates of outliers. Performances based on both artificial data and real metabolomics data indicate the superiority of our proposed kernel weight-based missing data imputation technique to the existing alternatives. For user convenience, an R package of the proposed kernel weight-based missing value imputation technique was developed, which is available at https://github.com/NishithPaul/tWLSA.

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