Missing Data Prediction using Correlation Genetic Algorithm and SVM Approach

Abstract

Data exists in large volume in the modern world, it becomes very useful when decoded correctly to inform decision making towards tackling real word issues. However, when the data is conflicting, it becomes a daunting task to get obtain information. Working on missing data has become a very important task in big data analysis. This paper considers the handling of the missing data using the Support Vector Machine (SVM) based on a technique called Correlation-Genetic Algorithm-SVM. This data is to be subjected to the SVM classification technique after identifying the attribute's correlation and application of the genetic algorithm. The application of the correlation enables a clear view of the attributes which are highly correlated within a particular dataset. The results indicate that apart from the SVM, the application of the proposed hybrid algorithm produces better outcomes identification rate and accuracy is considered. The proposed approach is also compared with depicts the Mean Identification rate of applying the neural network, the result indicate a consistent accuracy hence making it better.

An Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction

“…The first process of greatest data investigation entails all stages of processing that declare excellence and the setup of data as necessary for the process [ 10 ]. The data preprocessing process is appropriately accomplished to practice the large dataset for the requirements that were modelled through dissimilar kinds of algorithm [ 4 ]. The application of the data processing process is to produce data transformation, cleansing, integration, and normalization.…”

“…e data preprocessing process is appropriately accomplished to practice the large dataset for the requirements that were modelled through dissimilar kinds of algorithm [4].…”

“…The discretization applied for panel data attributes before extracting the association rules to overcome the main limitation of an association rule acquisition is that all the attributes must be categorical [ 4 ]. Even though discretization methods have two issues, the first one is to decide the correct number of intervals to apply because using too few intervals will make the data and result incomplete and introduce information loss [ 5 ].…”

“…On the other hand, using too many intervals, the data representation will be lower than the required level, resulting in noneffective intervals values. The second issue is that discretization methods make a clear theory about data distributions, and they do not work well when their assumptions are despoiled [ 4 ]. We identify the numerical correlations among attributes in the provided data to overcome the discretization issues and find repeated sequences of events for selecting relevant information about the relationships based on weight (the more effective value of an attribute) to uncover meaningful and hidden patterns by reducing the running time which in turns approaches the velocity aspect of big data.…”

A New Approach for Missing Data Imputation in Big Data Interface

“…Support vector machine (SVM) [2] algorithm avoids probability estimation on data which are stable. The kernel choosing has influence on the quality of the missing data imputation.…”