Boosting is a simple yet powerful modeling technique that is used in many machine learning and data mining related applications. In this paper, we propose a novel scale-space based boosting framework which applies scale-space theory for choosing the optimal regressors during the various iterations of the boosting algorithm. In other words, the data is considered at different resolutions for eac...

This paper describes a system to assist the selection of adequate reading materials to support European Portuguese teaching, especially as second language, while highlighting the key challenges on the selection of linguistic features for text difficulty (readability) classification. The system uses existing Natural Language Processing (NLP) tools to extract linguistic features from texts, which...

This paper discusses recent developments for pattern recognition focusing on boosting approach in machine learning. The statistical properties such as Bayes risk consistency for several loss functions are discussed in a probabilistic framework. There are a number of loss functions proposed for different purposes and targets. A unified derivation is given by a generator function U which naturall...

In this paper, kernel feature selection is proposed to improve generalization performance of boosting classifiers. Kernel feature Selection attains the feature selection and model selection at the same time using a simple selection algorithm. The algorithm automatically selects a subset of kernel features for each classifier and combines them according to the LogitBoost algorithm. The system em...

Boosting algorithms for classification are based on altering the initial distribution assumed to underly a given example set. The idea of knowledge-based sampling (KBS) is to sample out prior knowledge and previously discovered patterns to achieve that subsequently applied data mining algorithms automatically focus on novel patterns without any need to adjust the base algorithm. This sampling s...

We present a thorough investigation on using machine learning to construct effective personalized anti-spam filters. The investigation includes four learning algorithms, Naive Bayes, Flexible Bayes, LogitBoost, and Support Vector Machines, and four datasets, constructed from the mailboxes of different users. We discuss the model and search biases of the learning algorithms, along with worst-cas...

The aim of our research is to develop a scalable automatic why question answering system for English based on supervised method that uses part of speech analysis. The prior approach consisted in building a why-classifier using function words. This paper investigates the performance of combining supervised data mining methods with various feature selection strategies in order to obtain a more ac...

This paper presents Fuzzy-UCS, a Michigan-style Learning Fuzzy-Classifier System designed for supervised learning tasks. FuzzyUCS combines the generalization capabilities of UCS with the good interpretability of fuzzy rules to evolve highly accurate and understandable rule sets. Fuzzy-UCS is tested on a large collection of real-world problems, and compared to UCS and three highly-used machine l...

It is known that Boosting can be interpreted as a gradient descent technique to minimize an underlying loss function. Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which is proved to be very sensitive to random noise/outliers. Therefore, several Boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to improve the robus...

Due to the rapid advancement of electronic commerce technology, there is a great and dramatic increase in credit card transactions. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising; to detect credit card frauds in electronic transactions becomes the focus of risk of control of banks. The propos...