69,094 research outputs found
Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in Meta-Mining
The notion of meta-mining has appeared recently and extends the traditional
meta-learning in two ways. First it does not learn meta-models that provide
support only for the learning algorithm selection task but ones that support
the whole data-mining process. In addition it abandons the so called black-box
approach to algorithm description followed in meta-learning. Now in addition to
the datasets, algorithms also have descriptors, workflows as well. For the
latter two these descriptions are semantic, describing properties of the
algorithms. With the availability of descriptors both for datasets and data
mining workflows the traditional modelling techniques followed in
meta-learning, typically based on classification and regression algorithms, are
no longer appropriate. Instead we are faced with a problem the nature of which
is much more similar to the problems that appear in recommendation systems. The
most important meta-mining requirements are that suggestions should use only
datasets and workflows descriptors and the cold-start problem, e.g. providing
workflow suggestions for new datasets.
In this paper we take a different view on the meta-mining modelling problem
and treat it as a recommender problem. In order to account for the meta-mining
specificities we derive a novel metric-based-learning recommender approach. Our
method learns two homogeneous metrics, one in the dataset and one in the
workflow space, and a heterogeneous one in the dataset-workflow space. All
learned metrics reflect similarities established from the dataset-workflow
preference matrix. We demonstrate our method on meta-mining over biological
(microarray datasets) problems. The application of our method is not limited to
the meta-mining problem, its formulations is general enough so that it can be
applied on problems with similar requirements
Pairwise meta-rules for better meta-learning-based algorithm ranking
In this paper, we present a novel meta-feature generation method in the context of meta-learning, which is based on rules that compare the performance of individual base learners in a one-against-one manner. In addition to these new meta-features, we also introduce a new meta-learner called Approximate Ranking Tree Forests (ART Forests) that performs very competitively when compared with several state-of-the-art meta-learners. Our experimental results are based on a large collection of datasets and show that the proposed new techniques can improve the overall performance of meta-learning for algorithm ranking significantly. A key point in our approach is that each performance figure of any base learner for any specific dataset is generated by optimising the parameters of the base learner separately for each dataset
Algorithm Selection Framework for Cyber Attack Detection
The number of cyber threats against both wired and wireless computer systems
and other components of the Internet of Things continues to increase annually.
In this work, an algorithm selection framework is employed on the NSL-KDD data
set and a novel paradigm of machine learning taxonomy is presented. The
framework uses a combination of user input and meta-features to select the best
algorithm to detect cyber attacks on a network. Performance is compared between
a rule-of-thumb strategy and a meta-learning strategy. The framework removes
the conjecture of the common trial-and-error algorithm selection method. The
framework recommends five algorithms from the taxonomy. Both strategies
recommend a high-performing algorithm, though not the best performing. The work
demonstrates the close connectedness between algorithm selection and the
taxonomy for which it is premised.Comment: 6 pages, 7 figures, 1 table, accepted to WiseML '2
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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 “meta-features” 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%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models
Predictive response-relevant clustering of expression data provides insights into disease processes
This article describes and illustrates a novel method of microarray data analysis that couples model-based clustering and binary classification to form clusters of ;response-relevant' genes; that is, genes that are informative when discriminating between the different values of the response. Predictions are subsequently made using an appropriate statistical summary of each gene cluster, which we call the ;meta-covariate' representation of the cluster, in a probit regression model. We first illustrate this method by analysing a leukaemia expression dataset, before focusing closely on the meta-covariate analysis of a renal gene expression dataset in a rat model of salt-sensitive hypertension. We explore the biological insights provided by our analysis of these data. In particular, we identify a highly influential cluster of 13 genes-including three transcription factors (Arntl, Bhlhe41 and Npas2)-that is implicated as being protective against hypertension in response to increased dietary sodium. Functional and canonical pathway analysis of this cluster using Ingenuity Pathway Analysis implicated transcriptional activation and circadian rhythm signalling, respectively. Although we illustrate our method using only expression data, the method is applicable to any high-dimensional datasets
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