Proposing Logical Table Constructs for Enhanced Machine Learning Process

doi:10.1109/ACCESS.2018.2866046

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Proposing Logical Table Constructs for Enhanced Machine Learning Process

2018-01-01

Muhammad Fahim Uddin *

Syed S. Rizvi

Abdul Razaque

DOI：10.1109/ACCESS.2018.2866046

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Abstract

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Machine learning (ML) has shown enormous potential in various domains with the wide variations of underlying data types. Because of the miscellany in the data sets and the features, ML classifiers often suffer from challenges, such as feature miss-classification, unfit algorithms, low accuracy, overfitting, underfitting, extreme bias, and high predictive errors. Through the lens of related study and latest progress in the field, this paper presents a novel scheme to construct logical table (LT) unit with two internal sub-modules for algorithm blend and feature engineering. The LT unit works in the deepest layer of an enhanced ML engine engineering (eMLEE) process. eMLEE consists of several low-level modules to enhance the ML classifier progression. A unique engineering approach is adopted in eMLEE to blend various algorithms, enhance the feature engineering, construct a weighted performance metric, and augment the validation process. The LT is an in-memory logical component, that governs the progress of eMLEE, regulates the model metrics, improves the parallelism, and keep tracks of each module of eMLEE as the classifier learns. Optimum fitness of the model with parallel check, validate, insert, delete, and update mechanism in 3-D logical space via structured schemas in the LT is obtained. The LT unit is developed in Python, C#, and R libraries and tested using miscellaneous data sets. Results are created using GraphPad Prism, SigmaPlot, Plotly, and MS Excel software. To support the built and implementation of the proposed scheme, complete mathematical models along with the algorithms, and necessary illustrations are provided in this paper. To show the practicality of the proposed scheme, several simulation results are presented with a comprehensive analysis of the outcomes for the metrics of the model that the LT regulates with improved outcomes.

Keywords:

Big data

predictive modeling

data mining

machine learning

algorithm

parallel processing of machine learning metrics reading

model tuning

algorithm blending

optimum fitting

feature engineering

overfitting

eMLEE

logical table

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