BACKGROUND Microarray techniques provide promising tools for cancer diagnosis using gene expression profiles. However, molecular diagnosis based on high-throughput platforms presents great challenges due to the overwhelming number of variables versus the small sample size and the complex nature of multi-type tumors. Support vector machines (SVMs) have shown superior performance in cancer classi...

Most classification problems associate a single class to each example or instance. However, there are many classification tasks where each instance can be associated with one or more classes. This group of problems represents an area known as multi-label classification. One typical example of multi-label classification problems is the classification of documents, where each document can be assi...

Most software maintenance and evolution tasks require developers to understand the source code of their systems. Software usually inspect class comments gain knowledge about program behavior, regardless programming language they are using. Unfortunately, (i) different languages present language-specific commenting notations/guidelines; (ii) projects often lacks that adequately describe which co...

In this paper we investigate the performance of pairwise (or round robin) classification, originally a technique for turning multi-class problems into two-class problems, as a general ensemble technique. In particular, we show that the use of round robin ensembles will also increase the classification performance of decision tree learners, which could directly handle multi-class problems. The p...

One of the most important artificial immune algorithms is negative selection algorithm, which is an anomaly detection and pattern recognition technique; however, recent research has shown the successful application of this algorithm in data classification. Most of the negative selection methods consider deterministic boundaries to distinguish between self and non-self-spaces. In this paper, two...

In the context of motor imagery (MI)-based brain-computer interface (BCI) systems, a great amount research has been studied for attaining higher classification performance by extracting discriminative features from MI-based electroencephalogram (EEG) signals. this study, we propose an innovative approach classifying multi-class MI-EEG signals, which consists signal processing technique based on...

Recently several methods were proposed for the improvement of multi-label classification performance by using constraints on labels. Such constraints are based on dependencies between classes often present in multi-label data and can be mined as association rules from training data. The rules are then applied in a post-processing step to correct the classifier predictions. Due to properties of ...

The binary support vector machines (SVMs) have been extensively investigated. However their extension to a multi-classification model is still an on-going research. In this paper we present an extension of the binary support vector machines (SVMs) for the k > 2 class problems. The SVM model as originally proposed requires the construction of several binary SVM classifiers to solve the multi-cla...

The Winnow family of learning algorithms can cope well with large numbers of features and is tolerant to variations in document length, which makes it suitable for classifying large collections of large documents, like patent applications. Both the large size of the documents and the large number of available training documents for each class make this classification task qualitatively differen...

In this paper, we propose a method of multiple kernel learning (MKL) to inherently deal with multi-class classification problems. The performances of kernel-based classification methods depend on the employed kernel functions, and it is difficult to predefine the optimal kernel. In the framework of MKL, multiple types of kernel functions are linearly integrated with optimizing the weights for t...