background: rna molecules play many important regulatory, catalytic and structuralroles in the cell, and rna secondary structure prediction with pseudoknots is one themost important problems in biology. an rna pseudoknot is an element of the rna secondary structure in which bases of a single-stranded loop pair with complementary basesoutside the loop. modeling these nested structures (pseudokno...

In the fields of data mining and machine learning the amount of data available for building classifiers is growing very fast. Therefore, there is a great need for algorithms that are capable of building classifiers from very-large datasets and, simultaneously, being computationally efficient and scalable. One possible solution is to employ parallelism to reduce the amount of time spent in build...

Cross-validation is a useful and generally applicable technique often employed in machine learning, including decision tree induction. An important disadvantage of straightforward implementation of the technique is its computational overhead. In this paper we show that, for decision trees, the computational overhead of cross-validation can be reduced significantly by integrating the crossvalida...

Cross-validation is a useful and generally applicable technique often employed in machine learning, including decision tree induction. An important disadvantage of straightforward implementation of the technique is its computational overhead. In this paper we show that, for decision trees, the computational overhead of cross-validation can be reduced signiicantly by integrating the cross-valida...

Decision tree based schemes are widely used in designing high-speed packet classification algorithms. The primary objective is to construct a decision tree with minimal storage and searching time complexity. In this paper, we proposed a novel decision tree packet classification algorithm based on Efficient Multiple Bit Selection (EMBS). In the proposed algorithm, prefix fields are transformed t...

Patient-specific models are constructed to take advantage of the particular features of the patient case of interest compared to commonly used population-wide models that are constructed to perform well on average on all cases. We introduce two patient-specific algorithms that are based on the decision tree paradigm. These algorithms construct a decision path specific for each patient of intere...