Petre Lameski

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Collecting data at regular time nowadays is ubiquitous. The most widely used type of data that is being collected and analyzed is financial data and sensor readings. Various businesses have realized that financial time series analysis is a powerful analytical tool that can lead to competitive advantages. Likewise, sensor networks generate time series and if(More)
Feature selection is important phase in machine learning and in the case of multi-label classification, it can be considerably challenging. In like manner, finding the best subset of good features is involved and difficult when the dataset has significantly large number of features (more than a thousand). In this paper we address the problem of feature(More)
Wireless Sensor and Actor Networks (WSANs) have received increased attention from the research community in the recent years. This is mainly because as an extension to Wireless Sensor Networks(WSN), they have the ability to actively participate in the environment trough the actors. However, this introduces new challenges as to how to transfer commands(More)
Ambient Assisted Living (AAL) is promising to become a supplement of the current care models, providing enhanced living experience to people within context-aware homes and smart environments. Activity recognition based on sensory data in AAL systems is an important task because: it can be used for estimation of levels of physical activity; can lead to(More)
Nowadays, companies collect data at an increasingly high rate to the extent that traditional implementation of algorithms cannot cope with it in reasonable time. On the other hand, analysis of the available data is a key to the business success. In a Big Data setting tasks like feature selection, finding discretization thresholds of continuous data,(More)
Machine learning has received increased interest by both the scientific community and the industry. Most of the machine learning algorithms rely on certain distance metrics that can only be applied to numeric data. This becomes a problem in complex datasets that contain heterogeneous data consisted of numeric and nominal (i.e. categorical) features. Thus(More)
In several NoSQL database systems, among which is HBase, only one index is available for the tables, which is also the row key and the clustered index. Using other indexes does not come out of the box. As a result, the row key design is the most important thing when designing tables, because an inappropriate design can lead to detrimental consequences on(More)
In this paper we present our submission to the AAIA'16 Data Mining Challenge, where the objective was to predict dangerous seismic events based on hourly aggregated readings from different sensor and recent mining expert assessment of the conditions in the mine. During the course of the competition we have exploited a framework for automatic feature(More)
Transformation of features is a common task in the data preprocessing stage while solving data mining and classification problems. Many classification algorithms have preference of continual attributes over nominal attributes, and sometimes the distance between different data points cannot be estimated if the values of the attributes are not continual and(More)