This research describes a non-interactive process that applies several forms of computational intelligence to classifying biopsy lung tissue samples. Three types of lung cancer evaluated (squamous cell carcinoma, adenocarcinoma, and bronchioalveolar carcinoma) together account for 65–70% of diagnoses. Accuracy achieved supports hypothesis that an accurate predictive model is generated from trai...

Efficient and robust fault detection and diagnosis (FDD) can potentially play an important role in developing building management systems (BMS) for high performance buildings. Our research indicates that, in comparison to traditional model-based or data-driven methods, the combination of time series modeling and machine learning techniques produces higher accuracy and lower false alarm rates in...

This paper describes University of Leipzig’s approach to SemEval-2013 task 2B on Sentiment Analysis in Twitter: message polarity classification. Our system is designed to function as a baseline, to see what we can accomplish with well-understood and purely data-driven lexical features, simple generalizations as well as standard machine learning techniques: We use one-against-one Support Vector ...

This work explores the use of Support Vector Machines (SVM) for topic classification of conversations. An All-vs-One SVM system is used as the baseline. Several methods in feature weight scaling and feature selection are compared. Results suggest that the conversation domain requires a different set of methods from the written text domain. Finally, a feature selection method based on hierarchic...

The classification of texts is one of the most important cross-application technologies in information management. It is relevant to many tasks, including text filtering, information retrieval, and information extraction. In this paper, we describe the development and performance of a robust text classification suite, XM-XtraClass, suitable for industrial use. The classification components use ...

Handwriting analysis is a method to predict personality of an author and to better understand the writer. Allograph and allograph combination analysis is a scientific method of writer identification and evaluating the behavior. To make this computerized we considered six main different types of features: (i) size of letters, (ii) slant of letters and words, (iii) baseline, (iv) pen pressure, (v...

We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametriza...

Despite the increased awareness that exploiting the large amount of semantic data requires statistics-based inference capabilities, only little work can be found on this direction in the Semantic Web research. On semantic data, supervised approaches, particularly kernel-based Support Vector Machines (SVM), are promising. However, obtaining the right features to be used in kernels is an open pro...

Introduction There were several stand-alone vector surveillance applications being used by the New York State Department of Health (NYSDOH) to support the reporting of mosquito, bird, and mammal surveillance and infection information implemented in early 2000s in response to West Nile virus. In subsequent years, the Electronic Clinical Laboratory Reporting System (ECLRS) and the Communicable Di...