This paper presents a novel feature selection method for classification of high dimensional data, such as those produced by microarrays. It includes a partial supervision to smoothly favor the selection of some dimensions (genes) on a new dataset to be classified. The dimensions to be favored are previously selected from similar datasets in large microarray databases, hence performing inductive...

Feature selection is a useful pre-processing technique for solving classification problems. The challenge of solving the feature selection problem lies in applying evolutionary algorithms capable of handling the huge number of features typically involved. Generally, given classification data may contain useless, redundant or misleading features. To increase classification accuracy, the primary ...

In this paper we present two very popular aspects in supervised Machine Learning algorithms: feature selection and active learning paradigm. Feature selection algorithms are widely used in Machine Learning to reduce the feature space representing given data samples. Active learning is very popular supervised Machine Learning technique that has been effectively used in Text Classification tasks ...

Along with the fourth industrial revolution, research in biomedical engineering field is being actively conducted. Among these fields, brain–computer interface (BCI) research, which studies direct interaction between brain and external devices, spotlight. However, case of electroencephalograph (EEG) data measured through BCI, there are a huge number features, can lead to many difficulties analy...

Considerable research work have been conducted towards Intrusion Detection Systems (IDSs) as well as feature selection. IDS guard a system from attack, misuse, and compromise. It can also screen network activity. Network traffic observing and extent is increasingly regarded as an vital role for understanding and improving the performance and security of our cyber infrastructure. In this researc...

Natural scene text classification is considered to be a challenging task because of diversified set image contents, presence degradations including noise, low contrast/resolution and the random appearance foreground (font, style, sizes orientations) background properties. Above all, high dimension input image's feature space another major problem in such tasks. This work aimed tackle these prob...

In the context of big data, granular computing has recently been implemented by some mathematical tools, especially Rough Set Theory (RST). As a key topic rough set theory, feature selection investigated to adapt related concepts RST deal with large amounts leading development distributed version. However, despite its scalability, version faces challenge tied partitioning search space in enviro...

Feature selection is commonly employed for identifying the top n features that significantly contribute to desired prediction, example, find 50 or 100 genes responsible lung kidney cancer out of 50,000 genes. Thus, it a huge time- and resource-consuming practice. In this work, we propose divide-and-conquer technique with fuzzy backward feature elimination (FBFE) helps important quickly accurate...

The exact forecast of heart disease is necessary to proficiently treating cardiovascular patients before a failure happens. Assuming we talk about artificial intelligence (AI) techniques, can be accomplished utilizing an ideal AI model with rich medical services information on diseases. To begin with, the feature extraction technique, gradient boosting-based sequential selection (GBSFS) applied...