11,122 research outputs found
Evolving Ensemble-Clustering to a Feedback-Driven Process
Data clustering is a highly used knowledge extraction technique and is applied in more and more application domains. Over the last years, a lot of algorithms have been proposed that are often complicated and/or tailored to specific scenarios. As a result, clustering has become a hardly accessible domain for non-expert users, who face major difficulties like algorithm selection and parameterization. To overcome this issue, we develop a novel feedback-driven clustering process using a new perspective of clustering. By substituting parameterization with user-friendly feedback and providing support for result interpretation, clustering becomes accessible and allows the step-by-step construction of a satisfying result through iterative refinement
Evolving Ensemble-Clustering to a Feedback-Driven Process
Abstract—Data clustering is a highly used knowledge extrac-tion technique and is applied in more and more application domains. Over the last years, a lot of algorithms have been proposed that are often complicated and/or tailored to specific scenarios. As a result, clustering has become a hardly accessible domain for non-expert users, who face major difficulties like al-gorithm selection and parameterization. To overcome this issue, we develop a novel feedback-driven clustering process using a new perspective of clustering. By substituting parameterization with user-friendly feedback and providing support for result interpretation, clustering becomes accessible and allows the step-by-step construction of a satisfying result through iterative refinement. Keywords-ensemble-clustering; feedback; visualization I
Next challenges for adaptive learning systems
Learning from evolving streaming data has become a 'hot' research topic in the last decade and many adaptive learning algorithms have been developed. This research was stimulated by rapidly growing amounts of industrial, transactional, sensor and other business data that arrives in real time and needs to be mined in real time. Under such circumstances, constant manual adjustment of models is in-efficient and with increasing amounts of data is becoming infeasible. Nevertheless, adaptive learning models are still rarely employed in business applications in practice. In the light of rapidly growing structurally rich 'big data', new generation of parallel computing solutions and cloud computing services as well as recent advances in portable computing devices, this article aims to identify the current key research directions to be taken to bring the adaptive learning closer to application needs. We identify six forthcoming challenges in designing and building adaptive learning (pre-diction) systems: making adaptive systems scalable, dealing with realistic data, improving usability and trust, integrat-ing expert knowledge, taking into account various application needs, and moving from adaptive algorithms towards adaptive tools. Those challenges are critical for the evolving stream settings, as the process of model building needs to be fully automated and continuous.</jats:p
A Structural Model for Fluctuations in Financial Markets
In this paper we provide a comprehensive analysis of a structural model for
the dynamics of prices of assets traded in a market originally proposed in [1].
The model takes the form of an interacting generalization of the geometric
Brownian motion model. It is formally equivalent to a model describing the
stochastic dynamics of a system of analogue neurons, which is expected to
exhibit glassy properties and thus many meta-stable states in a large portion
of its parameter space. We perform a generating functional analysis,
introducing a slow driving of the dynamics to mimic the effect of slowly
varying macro-economic conditions. Distributions of asset returns over various
time separations are evaluated analytically and are found to be fat-tailed in a
manner broadly in line with empirical observations. Our model also allows to
identify collective, interaction mediated properties of pricing distributions
and it predicts pricing distributions which are significantly broader than
their non-interacting counterparts, if interactions between prices in the model
contain a ferro-magnetic bias. Using simulations, we are able to substantiate
one of the main hypotheses underlying the original modelling, viz. that the
phenomenon of volatility clustering can be rationalised in terms of an
interplay between the dynamics within meta-stable states and the dynamics of
occasional transitions between them.Comment: 16 pages, 8 (multi-part) figure
Report from the Tri-Agency Cosmological Simulation Task Force
The Tri-Agency Cosmological Simulations (TACS) Task Force was formed when
Program Managers from the Department of Energy (DOE), the National Aeronautics
and Space Administration (NASA), and the National Science Foundation (NSF)
expressed an interest in receiving input into the cosmological simulations
landscape related to the upcoming DOE/NSF Vera Rubin Observatory (Rubin),
NASA/ESA's Euclid, and NASA's Wide Field Infrared Survey Telescope (WFIRST).
The Co-Chairs of TACS, Katrin Heitmann and Alina Kiessling, invited community
scientists from the USA and Europe who are each subject matter experts and are
also members of one or more of the surveys to contribute. The following report
represents the input from TACS that was delivered to the Agencies in December
2018.Comment: 36 pages, 3 figures. Delivered to NASA, NSF, and DOE in Dec 201
Evolving Ensemble Fuzzy Classifier
The concept of ensemble learning offers a promising avenue in learning from
data streams under complex environments because it addresses the bias and
variance dilemma better than its single model counterpart and features a
reconfigurable structure, which is well suited to the given context. While
various extensions of ensemble learning for mining non-stationary data streams
can be found in the literature, most of them are crafted under a static base
classifier and revisits preceding samples in the sliding window for a
retraining step. This feature causes computationally prohibitive complexity and
is not flexible enough to cope with rapidly changing environments. Their
complexities are often demanding because it involves a large collection of
offline classifiers due to the absence of structural complexities reduction
mechanisms and lack of an online feature selection mechanism. A novel evolving
ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in
this paper. pENsemble differs from existing architectures in the fact that it
is built upon an evolving classifier from data streams, termed Parsimonious
Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism,
which estimates a localized generalization error of a base classifier. A
dynamic online feature selection scenario is integrated into the pENsemble.
This method allows for dynamic selection and deselection of input features on
the fly. pENsemble adopts a dynamic ensemble structure to output a final
classification decision where it features a novel drift detection scenario to
grow the ensemble structure. The efficacy of the pENsemble has been numerically
demonstrated through rigorous numerical studies with dynamic and evolving data
streams where it delivers the most encouraging performance in attaining a
tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System
- …