25,864 research outputs found
Fitting Prediction Rule Ensembles with R Package pre
Prediction rule ensembles (PREs) are sparse collections of rules, offering
highly interpretable regression and classification models. This paper presents
the R package pre, which derives PREs through the methodology of Friedman and
Popescu (2008). The implementation and functionality of package pre is
described and illustrated through application on a dataset on the prediction of
depression. Furthermore, accuracy and sparsity of PREs is compared with that of
single trees, random forest and lasso regression in four benchmark datasets.
Results indicate that pre derives ensembles with predictive accuracy comparable
to that of random forests, while using a smaller number of variables for
prediction
A review of associative classification mining
Associative classification mining is a promising approach in data mining that utilizes the
association rule discovery techniques to construct classification systems, also known as
associative classifiers. In the last few years, a number of associative classification algorithms
have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms
employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule
evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative
classification techniques with regards to the above criteria. Finally, future directions in associative
classification, such as incremental learning and mining low-quality data sets, are also
highlighted in this paper
A survey of cost-sensitive decision tree induction algorithms
The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field
Probabilistic Constraint Logic Programming
This paper addresses two central problems for probabilistic processing
models: parameter estimation from incomplete data and efficient retrieval of
most probable analyses. These questions have been answered satisfactorily only
for probabilistic regular and context-free models. We address these problems
for a more expressive probabilistic constraint logic programming model. We
present a log-linear probability model for probabilistic constraint logic
programming. On top of this model we define an algorithm to estimate the
parameters and to select the properties of log-linear models from incomplete
data. This algorithm is an extension of the improved iterative scaling
algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm
applies to log-linear models in general and is accompanied with suitable
approximation methods when applied to large data spaces. Furthermore, we
present an approach for searching for most probable analyses of the
probabilistic constraint logic programming model. This method can be applied to
the ambiguity resolution problem in natural language processing applications.Comment: 35 pages, uses sfbart.cl
An application of decision trees method for fault diagnosis of induction motors
Decision tree is one of the most effective and widely used methods for building
classification model. Researchers from various disciplines such as statistics, machine learning,
pattern recognition, and data mining have considered the decision tree method as an effective
solution to their field problems. In this paper, an application of decision tree method to classify the faults of induction motors is proposed. The original data from experiment is dealt with feature calculation to get the useful information as attributes. These data are then assigned the classes which are based on our experience before becoming data inputs for decision tree. The total 9 classes are defined. An implementation of decision tree written in Matlab is used for these data
Simple Regret Optimization in Online Planning for Markov Decision Processes
We consider online planning in Markov decision processes (MDPs). In online
planning, the agent focuses on its current state only, deliberates about the
set of possible policies from that state onwards and, when interrupted, uses
the outcome of that exploratory deliberation to choose what action to perform
next. The performance of algorithms for online planning is assessed in terms of
simple regret, which is the agent's expected performance loss when the chosen
action, rather than an optimal one, is followed.
To date, state-of-the-art algorithms for online planning in general MDPs are
either best effort, or guarantee only polynomial-rate reduction of simple
regret over time. Here we introduce a new Monte-Carlo tree search algorithm,
BRUE, that guarantees exponential-rate reduction of simple regret and error
probability. This algorithm is based on a simple yet non-standard state-space
sampling scheme, MCTS2e, in which different parts of each sample are dedicated
to different exploratory objectives. Our empirical evaluation shows that BRUE
not only provides superior performance guarantees, but is also very effective
in practice and favorably compares to state-of-the-art. We then extend BRUE
with a variant of "learning by forgetting." The resulting set of algorithms,
BRUE(alpha), generalizes BRUE, improves the exponential factor in the upper
bound on its reduction rate, and exhibits even more attractive empirical
performance
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