Prediction and correct voting is critical task in imbalance data multi-class classification. Accuracy and performance of multi-class depends on voting and prediction of new class data. Assigning of new class of imbalance data generate confusion and decrease the accuracy and performance of classifier. Various authors and research modified the multiclass classification approach such as one agains...

MOTIVATION Subcellular protein localization data are critical to the quantitative understanding of cellular function and regulation. Such data are acquired via observation and quantitative analysis of fluorescently labeled proteins in living cells. Differentiation of labeled protein from cellular artifacts remains an obstacle to accurate quantification. We have developed a novel hybrid machine-...

The security of computer networks plays a strategic role in modern computer systems. Intrusion Detection Systems (IDS) act as the ‘‘second line of defense’’ placed inside a protected network, looking for known or potential threats in network traffic and/or audit data recorded by hosts. We developed an Intrusion Detection System using LAMSTAR neural network to learn patterns of normal and intrus...

Evaluation of classifier performance is often based on statistical methods e.g. cross-validation tests. In these tests performance is often strongly related to or solely based on the accuracy of the classifier on a limited set of instances. The use of measure functions has been suggested as a promising approach to deal with this limitation. However, no usable implementation of a measure functi...

A resource limited immune approach (RLIA) was developed to evolve architecture and initial connection weights of multilayer neural networks. Then, with Back-Propagation (BP) algorithm, the appropriate connection weights can be found. The RLIA-BP classifier, which is derived from hybrid algorithm mentioned above, is demonstrated on SPOT multi-spectral image data, vowel data and Iris data effecti...

Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorith...

Although classifier systems have displayed performance levels equaling or exceeding those of other techniques on a variety of benchmark classification problems, they usually solve those problems with a very large number of classifiers. In most cases, a large portion of the final classifier set is unneeded or wrong, with behavior masked by the correctly-functioning rules in the system. Wilson de...

The recent i2b2 NLP Challenge smoking classification task offers a rare chance to compare different natural language processing techniques on actual clinical data. We compare the performance of a classifier which relies on semantic features generated by an unmodified version of MedLEE, a clinical NLP engine, to one using lexical features. We also compare the performance of supervised classifier...