Ensemble methods use multiple models to get better performance. Ensemble methods have
been used in multiple research fields such as computational intelligence, statistics and …

If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These
rule‐based, multifaceted, machine learning algorithms originated and have evolved in the …

Data mining techniques have been widely used in clinical decision support systems for
prediction and diagnosis of various diseases with good accuracy. These techniques have …

In classification, machine learning algorithms can suffer a performance bias when data sets
are unbalanced. Data sets are unbalanced when at least one class is represented by only a …

Artificial neural networks often achieve high classification accuracy rates, but they are
considered as black boxes due to their lack of explanation capability. This paper proposes …

Role-based access control (RBAC) in databases provides a valuable level of abstraction to
promote security administration at the business enterprise level. With the capacity for …

Intrusion detection systems (IDS) are commonly categorized into misuse based, anomaly
based and specification based IDS. Both misuse based IDS and anomaly based IDS are …

This paper focuses on an automated ANN classification system consisting of two modules:
an unsupervised Kohonen's Self-Organizing Mapping (SOM) neural network module, and a …

Evolutionary computation techniques have had limited capabilities in solving large-scale
problems due to the large search space demanding large memory and much longer training …

Negative Correlation Learning (NCL)[CHECK END OF SENTENCE],[CHECK END OF
SENTENCE] is a neural network ensemble learning algorithm which introduces a correlation …