The main objective of feature selection is to improve learning performance by selecting
concise and informative feature subsets, which presents a challenging task for machine …

A bit hurdle for financial institutions is to decide potential candidates to give a line of credit
identifying the right people without any credit risk. For such a crucial decision, past …

The feature selection problem involves selecting a subset of relevant features to enhance
the performance of machine learning models, crucial for achieving model accuracy. Its …

To solve the high-dimensionality issue and improve its accuracy in credit risk assessment, a
high-dimensionality-trait-driven learning paradigm is proposed for feature extraction and …

Feature Selection (FS) is one of the power solutions used in Machine Learning (ML)
problems, since it can help to remove irrelevant and redundant attributes, improve the …

Selecting the best features in a dataset improves accuracy and efficiency of classifiers in a
learning process. Datasets generally have more features than necessary, some of them …

A sensitive area such as credit risk assessment has always been a high priority and quite
difficult for financial institutions to make financial decisions. In order to have a more relevant …

Research using empirical and analytical approaches from the field of artificial intelligence
(AI) has found credit default to be a fascinating issue. The article shows a systematic …

In the context of smart agriculture, an early disease detection system is crucial to increase
agricultural yield. A disease detection system based on machine learning can be an …

The feature selection problem involves selecting a subset of relevant features to en-hance
the performance of machine learning models, crucial for achieving model accuracy. Its …