Feature selection is a popular topic. The main approaches to deal with it fall into the three categories of filters, wrappers and embedded methods. Advancement in algorithms, though proving fruitful, may be not enough. We propose integrate an explainable AI approach, based on Shapley values, provide more accurate information for feature selection. test our proposal real setting, which concerns ...

This paper proposes a feature selection method that combines various feature selection techniques. Feature selection has been realized as one of the most important processes in various applications, especially pattern classification problems. When too many attributes are involved, training a machine to classify patterns into their respective classes is seemingly impossible. Hence, selecting goo...

With an increase in the use of software, incidence bugs and resulting maintenance costs also increase. In open source projects, developer reassignment accounts for approximately 50%. Software can be reduced if appropriate developers are recommended to resolve bugs. this study, features extracted by applying feature selection each developer. These entered into CNN-LSTM algorithm learn model reco...

In order to improve classification accuracy and lower future computation data collecting costs, feature selection is the process of choosing most crucial features from a group attributes removing less or redundant ones. To narrow down that need be analyzed, variety procedures have been detailed in published publications. Chi-Square (CS), IG, Relief, GR, Symmetrical Uncertainty (SU), MI are six ...

This paper presents a feature selection technique based on distributional differences for efficient machine learning. Initial training data consists of data including many features and a target value. We classified them into positive and negative data based on the target value. We then divided the range of the feature values into 10 intervals and calculated the distribution of the intervals in ...

The study of feature selection methods has become an area of intensive research in pattern recognition. In this paper, a new feature selection approach, called cluster-based pattern discrimination (CPD), is introduced. Classes are independently partitioned into clusters to group together similar patterns: a different subspace is defined for each cluster by determining an optimal subset of featu...

Area of network traffic classification using application of machine learning has been increased enormously in recent years. Network traffic classificationis necessary today because of increase in no of users today in the internet and quality of service in the network. Network traffic classification algorithm works on various network traffic features. So in a huge amount of network traffic data ...

Classification technique can solve several problems in different fields like medicine, industry, business, science. Noise random error or variance in a measured variable.Reduction is one of the most popular techniques to remove noisy data. Two reduction technique are used for it (FS) Feature Selection and (FE) Feature Extraction. Feature Selection (FS) is a solution that involves finding a subs...