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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)
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)
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)
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)
In this paper, we describe an approach to the automatic plant identification task of the LifeCLEF 2014 lab. The image descrip-tors for all submitted runs were obtained using the bag-of-visual-words method. For the leaf scans, we use multiscale triangular shape descriptor and for the other plant organs Opponent SIFT extracted around points of interest(More)
Almost all of the machine learning problems require data preprocessing. This stage is especially important for problems where the datasets contain features of mixed types (i.e. nominal and numeric). An often practice in such cases is to transform each nominal features into many dummy (i.e. binary) features. Also many classification algorithms have(More)