In an era of growing data complexity and volume and the advent of big data, feature
selection has a key role to play in hel** reduce high-dimensionality in machine learning …

Traditionally, a bottleneck preventing the development of more intelligent systems was the
limited amount of data available. Nowadays, the total amount of information is almost …

This paper offers a comprehensive approach to feature selection in the scope of
classification problems, explaining the foundations, real application problems and the …

Lately, derived from the explosion of high dimensionality, researchers in machine learning
became interested not only in accuracy, but also in scalability. Although scalability of …

Classically, machine learning methods have used a single learning model to solve a given
problem. However, the technique of using multiple prediction models for solving the same …

With the proliferation of high-dimensional datasets across many application domains in
recent years, feature selection has become an important data mining task due to its …

Clustering analysis is widely used to stratify data in the same cluster when they are similar
according to the specific metrics. We can use the cluster analysis to group the CPU burst of a …

特徴選択アルゴリズムの安定性解析と堅牢な 特徴選択法の提案に関する研究 Study on Stability
Analy Page 1 2021 年度博士後期課程(ソフトウェア情報学)論文 特徴選択アルゴリズムの安定性解析 …

La selección de características se define como el proceso de detectar las características
relevantes y descartar las irrelevantes, con el objetivo de obtener un subconjunto de …

Data Visualization is a powerful paradigm to enhance data analysis. Different type of data
visualization techniques are widely used to facilitate graph analysis by taking advantage of …