Two complementary AI methods are used to improve the strength of the AI/ATP service for proving conjectures over the HOL Light and Flyspeck corpora. First, several schemes for frequency-based feature weighting are explored in combination with distanceweighted k-nearest-neighbor classifier. This results in 16% improvement (39.0% to 45.5% Flyspeck problems solved) of the overall strength of the s...

The application of learning approaches as Kernel or Instance Based methods to tree structured data requires the definition of similarity functions able to deal with such data. A new similarity function for nearest prototype classification in relational data that follows a tree structure is defined in this paper. Its main characteristic is its capability to weight the importance of the different...

Collaborative filtering uses a database about consumers’ preferences to make personal product recommendations and is achieving widespread success in E-Commerce nowadays. In this paper, we present several feature-weighting methods to improve the accuracy of collaborative filtering algorithms. Furthermore, we propose to reduce the training data set by selecting only highly relevant instances. We ...

In bioinformatics, we are interested in new techniques and advances in classification of biomedical documents for the hope of extracting useful biomedical knowledge out of the classification task. In this paper we introduce a feature weighting method for improving biomedical text classification. The method is effective in inducing weighted features from text data for classification. The weight ...

Nearest-neighbor algorithms are known to depend heavily on their distance metric. In this paper, we investigate the use of a weighted Euclidean metric in which the weight for each feature comes from a small set of options. We describe Diet, an algorithm that directs search through a space of discrete weights using cross-validation error as its evaluation function. Although a large set of possib...

Fuzzy ARTMAP with Relevance factor (FAMR) is a Fuzzy ARTMAP (FAM) neural architecture with the following property: Each training pair has a relevance factor assigned to it, proportional to the importance of that pair during the learning phase. Using a relevance factor adds more flexibility to the training phase, allowing ranking of sample pairs according to the confidence we have in the informa...

Unlike conventional data or text, Web pages typically contain a large amount of information that is not part of the main contents of the pages, e.g., banner ads, navigation bars, and copyright notices. Such irrelevant information (which we call Web page noise) in Web pages can seriously harm Web mining, e.g., clustering and classification. In this paper, we propose a novel feature weighting tec...

In Machine Learning Process, several issues arise in identifying a suitable and quality set of features from which a classification model for a particular domain to be constructed. This paper addresses the problem of feature selection for machine learning through discretization approach. RELIEF is considered to be one of the most successful algorithms for assessing the quality of features. RELI...

The Discriminative Feature Extraction (DFE) method provides an appropriate formalism for the design of the frontend feature extraction module in pattern classification systems. In the recent years, this formalism has been successfully applied to different speech recognition problems, like classification of vowels, classification of phonemes or isolated word recognition. The DFE formalism can be...

Feature weighting in supervised learning concerns the development of methods for quantifying the capability of features to discriminate instances from different classes. A popular method for this task, called RELIEF, generates a feature weight vector from a given training set, one weight for each feature. This is achieved by maximizing in a greedy way the sample margin defined on the nearest ne...