1,137 research outputs found
On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
With the coming data deluge from synoptic surveys, there is a growing need
for frameworks that can quickly and automatically produce calibrated
classification probabilities for newly-observed variables based on a small
number of time-series measurements. In this paper, we introduce a methodology
for variable-star classification, drawing from modern machine-learning
techniques. We describe how to homogenize the information gleaned from light
curves by selection and computation of real-numbered metrics ("feature"),
detail methods to robustly estimate periodic light-curve features, introduce
tree-ensemble methods for accurate variable star classification, and show how
to rigorously evaluate the classification results using cross validation. On a
25-class data set of 1542 well-studied variable stars, we achieve a 22.8%
overall classification error using the random forest classifier; this
represents a 24% improvement over the best previous classifier on these data.
This methodology is effective for identifying samples of specific science
classes: for pulsational variables used in Milky Way tomography we obtain a
discovery efficiency of 98.2% and for eclipsing systems we find an efficiency
of 99.1%, both at 95% purity. We show that the random forest (RF) classifier is
superior to other machine-learned methods in terms of accuracy, speed, and
relative immunity to features with no useful class information; the RF
classifier can also be used to estimate the importance of each feature in
classification. Additionally, we present the first astronomical use of
hierarchical classification methods to incorporate a known class taxonomy in
the classifier, which further reduces the catastrophic error rate to 7.8%.
Excluding low-amplitude sources, our overall error rate improves to 14%, with a
catastrophic error rate of 3.5%.Comment: 23 pages, 9 figure
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common methodologies for tool condition monitoring still rely on
batch approaches which cannot cope with a fast sampling rate of metal cutting
process. Furthermore they require a retraining process to be completed from
scratch when dealing with a new set of machining parameters. This paper
presents an online tool condition monitoring approach based on Parsimonious
Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly
flexible principle where both ensemble structure and base-classifier structure
can automatically grow and shrink on the fly based on the characteristics of
data streams. Moreover, the online feature selection scenario is integrated to
actively sample relevant input attributes. The paper presents advancement of a
newly developed ensemble learning algorithm, pENsemble+, where online active
learning scenario is incorporated to reduce operator labelling effort. The
ensemble merging scenario is proposed which allows reduction of ensemble
complexity while retaining its diversity. Experimental studies utilising
real-world manufacturing data streams and comparisons with well known
algorithms were carried out. Furthermore, the efficacy of pENsemble was
examined using benchmark concept drift data streams. It has been found that
pENsemble+ incurs low structural complexity and results in a significant
reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic
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
Classic and Bayesian Tree-Based Methods
Tree-based methods are nonparametric techniques and machine-learning methods for data prediction and exploratory modeling. These models are one of valuable and powerful tools among data mining methods and can be used for predicting different types of outcome (dependent) variable: (e.g., quantitative, qualitative, and time until an event occurs (survival data)). Tree model is called classification tree/regression tree/survival tree based on the type of outcome variable. These methods have some advantages over against traditional statistical methods such as generalized linear models (GLMs), discriminant analysis, and survival analysis. Some of these advantages are: without requiring to determine assumptions about the functional form between outcome variable and predictor (independent) variables, invariant to monotone transformations of predictor variables, useful for dealing with nonlinear relationships and high-order interactions, deal with different types of predictor variable, ease of interpretation and understanding results without requiring to have statistical experience, robust to missing values, outliers, and multicollinearity. Several classic and Bayesian tree algorithms are proposed for classification and regression trees, and in this chapter, we provide a review of these algorithms and appropriate criteria for determining the predictive performance of them
Estimating Classification Uncertainty of Bayesian Decision Tree Technique on Financial Data
Copyright © 2007 Springer. The final publication is available at link.springer.comBook title: Perception-based Data Mining and Decision Making in Economics and FinanceSummary
Bayesian averaging over classification models allows the uncertainty of classification outcomes to be evaluated, which is of crucial importance for making reliable decisions in applications such as financial in which risks have to be estimated. The uncertainty of classification is determined by a trade-off between the amount of data available for training, the diversity of a classifier ensemble and the required performance. The interpretability of classification models can also give useful information for experts responsible for making reliable classifications.
For this reason Decision Trees (DTs) seem to be attractive classification models.
The required diversity of the DT ensemble can be achieved by using the Bayesian model averaging all possible DTs. In practice, the Bayesian approach can be implemented on the base of a Markov Chain Monte Carlo (MCMC) technique of random sampling from the posterior distribution. For sampling large DTs, the MCMC method is extended by Reversible Jump technique which allows inducing DTs under given priors. For the case when the prior information on the DT size is unavailable, the sweeping technique defining the prior implicitly reveals a better performance. Within this chapter we explore the classification uncertainty of the Bayesian MCMC techniques on some datasets from the StatLog Repository and real financial data. The classification uncertainty is compared within an Uncertainty Envelope technique dealing with the class posterior distribution and a given confidence probability. This technique provides realistic estimates of the classification uncertainty which can be easily interpreted in statistical terms with the aim of risk evaluation
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