Feature Selection Based on Sampling and C4.5 Algorithm to Improve the Quality of Text Classification Using Naïve Bayes

Smart OptiSelect is a multi-objective evolutionary optimization and a machine learning based framework for software product lines feature selection. It serves in the direction of filling the gap between software product lines search based feature selection optimization and real life utilization by stakeholders. OptiSelect enables system analysts and project managers to select best features to implement to meet their dynamic and always changing objectives by offering plenty of multi-objective optimized solutions that complies with these objectives. Smart OptiSelect created the availability for providing various versions of result sets based on user experience in a more comprehensive working flow. Smart OptiSelect is enabled to interactively figure out user's preferences and help to reach more convenient solutions that should best draw out the user's desires and express his organization goals.

show abstract

“… Utilization of newly proposed 10-WS-C4.5-TDM-NB-TDMR [27] for user's preferences classification problem.…”

Section: Opti-selectmentioning

confidence: 99%

Smart OptiSelect Preference Based Innovative Framework for User-in-the-Loop Feature Selection in Software Product Lines

Yamany

El-Gamel²

2015

The 7th International Conference on Information Technology

View full text Add to dashboard Cite

show abstract

“…The results showed that the FS method had promising results. In addition, from other research works in feature selection include but not limited to [23][24]. Due to the limitations found with the available FS techniques such as high computational cost (as associated with wrapper-based FS techniques) and lower accuracy performance (as associated with filter-based techniques), the study proposes a two-step FS method.…”

mentioning

confidence: 99%

A two-step feature selection method for quranic text classification

Adeleke

Samsudin

Othman

et al. 2019

IJEECS

View full text Add to dashboard Cite

Feature selection is an integral phase in text classification problems. It is primarily applied in preprocessing text data prior to labeling. However, there exist some limitations with the FS techniques. The filter-based FS techniques have the drawback of lower accuracy performance while the wrapper-based techniques are highly computationally expensive to process. In this paper, a two-step FS method is presented. In the first step, chisquare (CH) filter-based technique is used to reduce the dimensionality of the feature set and then wrapper correlation-based (CFS) technique is employed in the second step to further select most relevant features from the reduced feature set. Specifically, the ultimate aim is to reduce the computational runtime while achieving high classification accuracy. Subsequently, the proposed method was applied in labeling instances of the input data (Quranic verses) using standard classifiers: naïve bayes (NB), support vector machine (SVM), decision trees (J48). The results report the proposed method achieved accuracy result of 93.6% at 4.17secs.

show abstract

A Method of Document Sensitivity Calculation Based on Semantic Dependency Analysis

Xie

2019

2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

View full text Add to dashboard Cite

Feature Selection Based on Sampling and C4.5 Algorithm to Improve the Quality of Text Classification Using Naïve Bayes

Cited by 3 publications

References 32 publications

Smart OptiSelect Preference Based Innovative Framework for User-in-the-Loop Feature Selection in Software Product Lines

Smart OptiSelect Preference Based Innovative Framework for User-in-the-Loop Feature Selection in Software Product Lines

A two-step feature selection method for quranic text classification

A Method of Document Sensitivity Calculation Based on Semantic Dependency Analysis

Contact Info

Product

Resources

About