One-class classification (OCC) algorithms aim to build classification models when the
negative class is either absent, poorly sampled or not well defined. This unique situation …

Intrusion Detection Systems (IDSs) are one of the key components for securing computing
infrastructures. Their objective is to protect against attempts to violate defense mechanisms …

Pattern recognition systems have been widely used in adversarial classification tasks like
spam filtering and intrusion detection in computer networks. In these applications a …

One-class support vector machine (OCSVM) is a commonly used one-class classification
method for tackling novelty detection problems. Unfortunately, employing the traditional …

As a commonly used ensemble method, AdaBoost has drawn much consideration in the
field of machine learning. However, AdaBoost is highly sensitive to outliers. The …

Adaptive boosting (AdaBoost) is a widely used technique to construct a stronger ensemble
classifier by combining a set of weaker ones. Zero-order fuzzy inference systems (FISs) are …

Prediction of organismal viability upon exposure to a nanoparticle in varying environments─
as fully specified at the molecular scale─ has emerged as a useful figure of merit in the …

Selecting the best classifier among the available ones is a difficult task, especially when only
instances of one class exist. In this work we examine the notion of combining one-class …

As part counts in high performance computing systems are projected to increase faster than
part reliabilities, there is increasing interest in enabling jobs to continue to execute in the …

The two-volume set LNCS 7367 and 7368 constitutes the refereed proceedings of the 9th
International Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July …