This paper presents a wrapper method for feature selection that combines Lazy Learning, racing and subsampling techniques. Lazy Learning (LL) is a local learning technique that, once a query is received, extracts a prediction by locally interpolating the neighboring examples of the query which are considered relevant according to a distance measure. Local learning techniques are often criticize...

In the feature subset selection problem a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention while ignoring the rest To achieve the best possible performance with a particular learning algorithm on a particular training set a feature subset selection method should consider how the algorithm and the training set interact We e...

We present general-purpose methods for recognizing certain types of structure in HTML documents. The methods are implemented using WHIRL, a "soft" logic that incorporates a notion of textual similarity developed in the information retrieval community. In an experimental evaluation on 82 Web pages, the structure ranked first by our method is "meaningful"--i.e., a structure that was used in a han...

Data mining is a form of knowledge discovery required for solving problems in a specific domain. Classification is a technique used for discovering class labels of unknown data. Different methods for classification exists like bayesian, decision trees, rule based, neural networks etc. Before applying any mining technique, irrelevant and redundant features needs to be removed. Filtering is done ...

In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a feature subset selection method should consider how the algorithm and the training set interact....

The paper introduces a feature selection wrapper designed specifically for Echo State Networks. It defines a feature scoring heuristics, applicable to generic subset search algorithms, which allows to reduce the need for model retraining with respect to wrappers in literature. The experimental assessment on real-word noisy sequential data shows that the proposed method can identify a compact se...

Supplementary Methods 6 MAPPING METHOD 6 General theory behind likelihood maximization 7 MLE for expected degrees κ 7 MLE for angular coordinates θ 8 MLE kernels 8 First MLE wrapper 8 Algorithm 1 9 Second MLE wrapper 9 Algorithm 2 10 Parameter estimation and finite size effects 10 Estimating γ 10 Estimating N and k̄ 11 Estimating β 12 DEALING WITH NEW-COMING ASs 12 SENSITIVITY TO MISSING LINKS 1...