In this study, we use the Extended Classifier System (XCS) to model the market behavior of financial time series, the purpose of which is to provide effective trading decision support. Several technical indicators and their firstand second-order derivatives are selected as the market descriptive variables, which are then used for XCS training. Then, the adaptive rules of the classifiers, which ...

With the popularity of mobile services, an effective context-aware mobile service adaptation is becoming more and more important for operators. In this paper, we propose a Co-evolution eXtended Classifier System (CXCS) to perform context-aware mobile service adaptation. Our key idea is to learn user context, match adaptation rule, and provide the best suitable mobile services for users. Differe...

While text classification has been identified for some time as a promising application area for Artificial Intelligence, so far few deployed applications have been described. In this paper we present a spam filtering system that uses example-based machine learning techniques to train a classifier from examples of spam and legitimate email. This approach has the advantage that it can personalise...

This paper describes our participation in the SemEval-2016 task 5, Aspect Based Sentiment Analysis (ABSA). We participated in two slots in the sentence level ABSA (Subtask 1) namely: aspect category extraction (Slot 1) and sentiment polarity extraction (Slot 3) in English Restaurants and Laptops reviews. For Slot 1, we applied different models for each domain. In the restaurants domain, we used...

We present a reduction framework from ordinal regression to binary classification based on extended examples. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranking rule from the binary classifier. A weighted 0/1 loss of the binary c...

Usually face classification applications suffer from two important problems: the number of training samples from each class is reduced, and the final system usually must be extended to incorporate new people to recognize. In this paper we introduce a face recognition method that extends a previous boosting-based classifier adding new classes and avoiding the need of retraining the system each t...

This paper describes the design and modeling of an artificial neural network (ANN) classifier using VHDL. This classifier is targeted primarily to classify the six different types of power quality disturbance. The high level architecture comprises of a control unit and a neural network datapath. The control unit is further divided into five interconnected sub modules: bus master, ram, pseudo ra...