67,467 research outputs found
Improved Heterogeneous Distance Functions
Instance-based learning techniques typically handle continuous and linear
input values well, but often do not handle nominal input attributes
appropriately. The Value Difference Metric (VDM) was designed to find
reasonable distance values between nominal attribute values, but it largely
ignores continuous attributes, requiring discretization to map continuous
values into nominal values. This paper proposes three new heterogeneous
distance functions, called the Heterogeneous Value Difference Metric (HVDM),
the Interpolated Value Difference Metric (IVDM), and the Windowed Value
Difference Metric (WVDM). These new distance functions are designed to handle
applications with nominal attributes, continuous attributes, or both. In
experiments on 48 applications the new distance metrics achieve higher
classification accuracy on average than three previous distance functions on
those datasets that have both nominal and continuous attributes.Comment: See http://www.jair.org/ for an online appendix and other files
accompanying this articl
Efficient intrusion detection scheme based on SVM
The network intrusion detection problem is the focus of current academic research. In this paper, we propose to use Support Vector Machine (SVM) model to identify and detect the network intrusion problem, and simultaneously introduce a new optimization search method, referred to as Improved Harmony Search (IHS) algorithm, to determine the parameters of the SVM model for better classification accuracy. Taking the general mechanism network system of a growing city in China between 2006 and 2012 as the sample, this study divides the mechanism into normal network system and crisis network system according to the harm extent of network intrusion. We consider a crisis network system coupled with two to three normal network systems as paired samples. Experimental results show that SVMs based on IHS have a high prediction accuracy which can perform prediction and classification of network intrusion detection and assist in guarding against network intrusion
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A novel improved model for building energy consumption prediction based on model integration
Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into âmeta-featuresâ to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%â31.6% for Case A and 16.2%â49.4% for Case B), generalization (improvement of 6.7%â29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%â34.1% for Case A and 1.8%â19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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
Pipelining the Fast Multipole Method over a Runtime System
Fast Multipole Methods (FMM) are a fundamental operation for the simulation
of many physical problems. The high performance design of such methods usually
requires to carefully tune the algorithm for both the targeted physics and the
hardware. In this paper, we propose a new approach that achieves high
performance across architectures. Our method consists of expressing the FMM
algorithm as a task flow and employing a state-of-the-art runtime system,
StarPU, in order to process the tasks on the different processing units. We
carefully design the task flow, the mathematical operators, their Central
Processing Unit (CPU) and Graphics Processing Unit (GPU) implementations, as
well as scheduling schemes. We compute potentials and forces of 200 million
particles in 48.7 seconds on a homogeneous 160 cores SGI Altix UV 100 and of 38
million particles in 13.34 seconds on a heterogeneous 12 cores Intel Nehalem
processor enhanced with 3 Nvidia M2090 Fermi GPUs.Comment: No. RR-7981 (2012
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