Carlos Abreu Ferreira

Learn More
In highly populated urban zones, it is common to notice headway deviations (HD) between pairs of buses. When these events occur in a bus stop, they often cause bus bunching (BB) in the following bus stops. Several proposals have been suggested to mitigate this problem. In this paper, we propose to find BBS (Bunching Black Spots) – sequences of bus stops(More)
Decision rules are one of the most expressive languages for machine learning. In this paper we present Adaptive Model Rules (AMRules), the first streaming rule learning algorithm for regression problems. In AMRules the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of attribute values.(More)
A SCADA-data based data mining approach to estimate wind turbine loads Prevalence of, risk factors for, and oxidative stress associated with Toxoplasma gondii antibodies among Egyptian asymptomatic blood donors Content independant metadata production as a machine learning problem Sahar Changuel and Nicolas Labroche
One of the major challenges in knowledge discovery is how to extract meaningful and useful knowledge from the complex structured data that one finds in scientific and technological applications. One approach is to explore the logic relations in the database and using, say, an inductive logic programming (ILP) algorithm find descriptive and expressive(More)
In this work, we introduce the MuSer, a propositional framework that explores temporal information available in multi-relational databases. At the core of this system is an encoding technique that translates the temporal information into a propositional sequence of events. By using this technique, we are able to explore the temporal information using a(More)
In this work we present an optimized version of XMuSer, an ILP based framework suitable to explore temporal patterns available in multi-relational databases. The main idea behind XMuSer consists of exploiting frequent sequence mining, an efficient and direct method to learn temporal patterns in the form of sequences. The efficiency of XMuSer comes from a(More)
The motivation for this work is the study and prediction of wind ramp events occurring in a large-scale wind farm located in the US Midwest. In this paper we introduce the SHREA framework, a stream-based model that continuously learns a discrete HMM model from wind power and wind speed measurements. We use a supervised learning algorithm to learn HMM(More)
Decision rules are one of the most expressive languages for machine learning. In this paper we present Adaptive Model Rules (AMRules), the first streaming rule learning algorithm for regression problems. In AMRules the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of attribute values.(More)