Fuzzy Clustering with Improved Swarm Optimization and Genetic Algorithm: Hybrid Approach

Naik, Bighnaraj; Mahapatra, Sarita; Nayak, Janmenjoy; Behera, Himansu Sekhar

doi:10.1007/978-981-10-3874-7_23

Cited by 4 publications

(2 citation statements)

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“…The fuzzy C is a hybrid method. Fuzzy C imputation method is combined with the genetic algorithm to estimate the missing values in data [32]. This method is mostly used in pattern recognition.…”

Section: Local Approachmentioning

confidence: 99%

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An Examination of Machine Learning Algorithms for Missing Values Imputation

2019

IJITEE

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In gene expression studies missing values have been a common problem. It has an important consequence on the explanation of the final data. Numerous Bioinformatics examination tools that are used for cancer prediction includes the dataset matrix. Hence, it is necessary to resolve this problem of missing values imputation. Our research paper presents a review of missing values imputation approaches. It represents the research and imputation of missing values in gene expression data. By using the local or global correlation of the data we focus mostly on the contrast of the algorithms. We considered the algorithms in a global, hybrid, local, and knowledge-based technique. Additionally, we presented the different approaches with a suitable assessment. The purpose of our review article is to focus on the developments of current techniques. For scientists rather applying different or newly develop algorithms with the identical functional goal. We want an adaptation of algorithms to the characteristics of the data".

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Section: Local Approachmentioning

confidence: 99%

“…These methods were tested on the data from the original function. The fuzzy models are found with least error among the real data and fuzzy model [32]. FCM allows feature vector to belong to all clusters with a fuzzy truth value (between 0 and 1).…”

Section: Local Approachmentioning

confidence: 99%