7,636 research outputs found
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection
In order to achieve high efficiency of classification in intrusion detection,
a compressed model is proposed in this paper which combines horizontal
compression with vertical compression. OneR is utilized as horizontal
com-pression for attribute reduction, and affinity propagation is employed as
vertical compression to select small representative exemplars from large
training data. As to be able to computationally compress the larger volume of
training data with scalability, MapReduce based parallelization approach is
then implemented and evaluated for each step of the model compression process
abovementioned, on which common but efficient classification methods can be
directly used. Experimental application study on two publicly available
datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the
classification using the compressed model proposed can effectively speed up the
detection procedure at up to 184 times, most importantly at the cost of a
minimal accuracy difference with less than 1% on average
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS
Many distributed machine learning frameworks have recently been built to
speed up the large-scale data learning process. However, most distributed
machine learning used in these frameworks still uses an offline algorithm model
which cannot cope with the data stream problems. In fact, large-scale data are
mostly generated by the non-stationary data stream where its pattern evolves
over time. To address this problem, we propose a novel Evolving Large-scale
Data Stream Analytics framework based on a Scalable Parsimonious Network based
on Fuzzy Inference System (Scalable PANFIS), where the PANFIS evolving
algorithm is distributed over the worker nodes in the cloud to learn
large-scale data stream. Scalable PANFIS framework incorporates the active
learning (AL) strategy and two model fusion methods. The AL accelerates the
distributed learning process to generate an initial evolving large-scale data
stream model (initial model), whereas the two model fusion methods aggregate an
initial model to generate the final model. The final model represents the
update of current large-scale data knowledge which can be used to infer future
data. Extensive experiments on this framework are validated by measuring the
accuracy and running time of four combinations of Scalable PANFIS and other
Spark-based built in algorithms. The results indicate that Scalable PANFIS with
AL improves the training time to be almost two times faster than Scalable
PANFIS without AL. The results also show both rule merging and the voting
mechanisms yield similar accuracy in general among Scalable PANFIS algorithms
and they are generally better than Spark-based algorithms. In terms of running
time, the Scalable PANFIS training time outperforms all Spark-based algorithms
when classifying numerous benchmark datasets.Comment: 20 pages, 5 figure
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