An Algorithm for Classifying Incomplete Data with Selective Bayes Classifiers

Chen, Jingnian; Xue, Xiaoping; Tian, Fengzhan; Huang, Houkuan

doi:10.1109/cisw.2007.4425530

Cited by 2 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since these methods rely on the assumption that data are Missing at Random (MAR) [17] or they treat the missing data as fixed known data [18], they suffer of dramatic decrease in accuracy. A full discussion can be found in [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…Along with the growth of the data and the need for solutions in the problem of missing data, there is a great necessity of computationally efficient and scalable algorithms able to extract useful information from data sets of very large size [22][23][24][25][26][27][28]. This is one of the main challenges in computational biology, since the tools and the methods capable of transforming the heterogeneous available data into biological knowledge [29] must be implemented efficiently and effectively on the available computer systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards a HPC-oriented parallel implementation of a learning algorithm for bioinformatics applications

D’Angelo

Rampone

2014

BMC Bioinformatics

View full text Add to dashboard Cite

BackgroundThe huge quantity of data produced in Biomedical research needs sophisticated algorithmic methodologies for its storage, analysis, and processing. High Performance Computing (HPC) appears as a magic bullet in this challenge. However, several hard to solve parallelization and load balancing problems arise in this context. Here we discuss the HPC-oriented implementation of a general purpose learning algorithm, originally conceived for DNA analysis and recently extended to treat uncertainty on data (U-BRAIN). The U-BRAIN algorithm is a learning algorithm that finds a Boolean formula in disjunctive normal form (DNF), of approximately minimum complexity, that is consistent with a set of data (instances) which may have missing bits. The conjunctive terms of the formula are computed in an iterative way by identifying, from the given data, a family of sets of conditions that must be satisfied by all the positive instances and violated by all the negative ones; such conditions allow the computation of a set of coefficients (relevances) for each attribute (literal), that form a probability distribution, allowing the selection of the term literals. The great versatility that characterizes it, makes U-BRAIN applicable in many of the fields in which there are data to be analyzed. However the memory and the execution time required by the running are of O(n3) and of O(n5) order, respectively, and so, the algorithm is unaffordable for huge data sets.ResultsWe find mathematical and programming solutions able to lead us towards the implementation of the algorithm U-BRAIN on parallel computers. First we give a Dynamic Programming model of the U-BRAIN algorithm, then we minimize the representation of the relevances. When the data are of great size we are forced to use the mass memory, and depending on where the data are actually stored, the access times can be quite different. According to the evaluation of algorithmic efficiency based on the Disk Model, in order to reduce the costs of the communications between different memories (RAM, Cache, Mass, Virtual) and to achieve efficient I/O performance, we design a mass storage structure able to access its data with a high degree of temporal and spatial locality. Then we develop a parallel implementation of the algorithm. We model it as a SPMD system together to a Message-Passing Programming Paradigm. Here, we adopt the high-level message-passing systems MPI (Message Passing Interface) in the version for the Java programming language, MPJ. The parallel processing is organized into four stages: partitioning, communication, agglomeration and mapping. The decomposition of the U-BRAIN algorithm determines the necessity of a communication protocol design among the processors involved. Efficient synchronization design is also discussed.ConclusionsIn the context of a collaboration between public and private institutions, the parallel model of U-BRAIN has been implemented and tested on the INTEL XEON E7xxx and E5xxx family of the CRESCO structure of Italian National Agency for New Tec...

show abstract

Section: Introductionmentioning

confidence: 99%