18,471 research outputs found
Data Sketches for Disaggregated Subset Sum and Frequent Item Estimation
We introduce and study a new data sketch for processing massive datasets. It
addresses two common problems: 1) computing a sum given arbitrary filter
conditions and 2) identifying the frequent items or heavy hitters in a data
set. For the former, the sketch provides unbiased estimates with state of the
art accuracy. It handles the challenging scenario when the data is
disaggregated so that computing the per unit metric of interest requires an
expensive aggregation. For example, the metric of interest may be total clicks
per user while the raw data is a click stream with multiple rows per user. Thus
the sketch is suitable for use in a wide range of applications including
computing historical click through rates for ad prediction, reporting user
metrics from event streams, and measuring network traffic for IP flows.
We prove and empirically show the sketch has good properties for both the
disaggregated subset sum estimation and frequent item problems. On i.i.d. data,
it not only picks out the frequent items but gives strongly consistent
estimates for the proportion of each frequent item. The resulting sketch
asymptotically draws a probability proportional to size sample that is optimal
for estimating sums over the data. For non i.i.d. data, we show that it
typically does much better than random sampling for the frequent item problem
and never does worse. For subset sum estimation, we show that even for
pathological sequences, the variance is close to that of an optimal sampling
design. Empirically, despite the disadvantage of operating on disaggregated
data, our method matches or bests priority sampling, a state of the art method
for pre-aggregated data and performs orders of magnitude better on skewed data
compared to uniform sampling. We propose extensions to the sketch that allow it
to be used in combining multiple data sets, in distributed systems, and for
time decayed aggregation
Deterministic Sampling and Range Counting in Geometric Data Streams
We present memory-efficient deterministic algorithms for constructing
epsilon-nets and epsilon-approximations of streams of geometric data. Unlike
probabilistic approaches, these deterministic samples provide guaranteed bounds
on their approximation factors. We show how our deterministic samples can be
used to answer approximate online iceberg geometric queries on data streams. We
use these techniques to approximate several robust statistics of geometric data
streams, including Tukey depth, simplicial depth, regression depth, the
Thiel-Sen estimator, and the least median of squares. Our algorithms use only a
polylogarithmic amount of memory, provided the desired approximation factors
are inverse-polylogarithmic. We also include a lower bound for non-iceberg
geometric queries.Comment: 12 pages, 1 figur
Evaluation methods and decision theory for classification of streaming data with temporal dependence
Predictive modeling on data streams plays an important role in modern data analysis, where data arrives continuously and needs to be mined in real time. In the stream setting the data distribution is often evolving over time, and models that update themselves during operation are becoming the state-of-the-art. This paper formalizes a learning and evaluation scheme of such predictive models. We theoretically analyze evaluation of classifiers on streaming data with temporal dependence. Our findings suggest that the commonly accepted data stream classification measures, such as classification accuracy and Kappa statistic, fail to diagnose cases of poor performance when temporal dependence is present, therefore they should not be used as sole performance indicators. Moreover, classification accuracy can be misleading if used as a proxy for evaluating change detectors with datasets that have temporal dependence. We formulate the decision theory for streaming data classification with temporal dependence and develop a new evaluation methodology for data stream classification that takes temporal dependence into account. We propose a combined measure for classification performance, that takes into account temporal dependence, and we recommend using it as the main performance measure in classification of streaming data
Adaptive Online Sequential ELM for Concept Drift Tackling
A machine learning method needs to adapt to over time changes in the
environment. Such changes are known as concept drift. In this paper, we propose
concept drift tackling method as an enhancement of Online Sequential Extreme
Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by
adding adaptive capability for classification and regression problem. The
scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme
that works well to handle real drift, virtual drift, and hybrid drift. The
AOS-ELM also works well for sudden drift and recurrent context change type. The
scheme is a simple unified method implemented in simple lines of code. We
evaluated AOS-ELM on regression and classification problem by using concept
drift public data set (SEA and STAGGER) and other public data sets such as
MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value
compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice
does not need hidden nodes increase, we address some issues related to the
increasing of the hidden nodes such as error condition and rank values. We
propose taking the rank of the pseudoinverse matrix as an indicator parameter
to detect underfitting condition.Comment: Hindawi Publishing. Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 8091267, 17 pages Received 29 January 2016,
Accepted 17 May 2016. Special Issue on "Advances in Neural Networks and
Hybrid-Metaheuristics: Theory, Algorithms, and Novel Engineering
Applications". Academic Editor: Stefan Hauf
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