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Naive Bayes is a probability-based classification method which is based on the assumption that attributes are conditionally mutually independent given the class label. Much research has been focused on improving the accuracy of Naïve Bayes via eager learning. In this paper, we propose a novel lazy learning algorithm, Selective Neighbourhood based Naïve(More)
MOTIVATION Protein complexes are of great importance for unraveling the secrets of cellular organization and function. The AP-MS technique has provided an effective high-throughput screening to directly measure the co-complex relationship among multiple proteins, but its performance suffers from both false positives and false negatives. To computationally(More)
The automatic recognition of sound events by computers is an important aspect of emerging applications such as automated surveillance, machine hearing and auditory scene understanding. Recent advances in machine learning, as well as in computational models of the human auditory system, have contributed to advances in this increasingly popular research(More)
Concept lattice model, the core structure in Formal Concept Analysis, has been successfully applied in software engineering and knowledge discovery. In this paper, we integrate the simple base classifier (Naïve Bayes or Nearest Neighbor) into each node of the concept lattice to form a new composite classifier. We develop two new classification systems, CLNB(More)
With the increasing availability of diverse biological information for proteins, integration of heterogeneous data becomes more useful for many problems in proteomics, such as annotating protein functions, predicting novel protein-protein interactions and so on. In this paper, we present an integrative approach called InteHC (Integrative Hierarchical(More)
Ensemble methods have proved effective to achieve higher accuracy. Some simple ensemble methods, such as Bagging, work well with unstable base algorithms, but fail with stable ones. The reason is that such methods achieve higher accuracy by reducing only the variance of the base algorithms. It does not touch the bias. Here, we propose a novel ensemble(More)