Data mining is a powerful method to extract knowledge from data. Raw data faces various
challenges that make traditional method improper for knowledge extraction. Data mining is …

Feature selection techniques have been used as the workhorse in biomarker discovery
applications for a long time. Surprisingly, the stability of feature selection with respect to …

High-dimensional data analysis is a challenge for researchers and engineers in the fields of
machine learning and data mining. Feature selection provides an effective way to solve this …

The airline industry is moving toward proactive risk management, which aims to identify and
mitigate risks before accidents occur. However, existing methods for such efforts are limited …

With the rapid development in technology, large amounts of high-dimensional data have
been generated. This high dimensionality including redundancy and irrelevancy poses a …

Many feature selection algorithms have been proposed in the past focusing on improving
classification accuracy. In this work, we point out the importance of stable feature selection …

Features extracted from real world applications increase dramatically, while machine
learning methods decrease their performance given the previous scenario, and feature …

Distance metric is the basis of many learning algorithms, and its effectiveness usually has a
significant influence on the learning results. In general, measuring distance for numerical …

Feature selection is an important topic in data mining, especially for high dimensional
datasets. Filtering techniques in particular have received much attention, but detailed …

In the microarray-based approach for automated cancer diagnosis, the application of the
traditional k-nearest neighbors kNN algorithm suffers from several difficulties such as the …