The algorithm used by the authors in the bird identification task of LifeCLEF 2016 consists in creating a dictionary of MFCC-based words using k-means clustering, computing histograms of these words over short audio segments and feeding them to a random forest classifier. The official score achieved is 0.15 MAP.

The inactive student rate is becoming a major problem in most open universities worldwide. In Indonesia, roughly 36% of students were found to be inactive, in 2005. Data mining had been successfully employed to solve problems in many domains, such as for educational purposes. We are proposing a method for preventing inactive students by mining knowledge from student record systems with several ...

Binarization techniques are widely used to solve multi-class classification problems. These techniques reduce the classification complexity of multi-class classification problems by dividing the original data set into two-class segments or replicas. Then a set of simpler classifiers are learnt from the two-class segments or replicas. The outputs from these classifiers are combined for final cla...

In this paper, a Random Forest classifier was used to predict leak locations for two differently sized water distribution networks based on pressure sensor measurements. The prediction model is trained simulated scenarios with randomly chosen parameters - location, size, and base node demand uncertainty. Leak localization methods found in literature that rely numerical simulations can only netw...

In ensemble methods the aggregation of multiple unstable classifiers often leads to reduce the misclassification rates substantially in many applications and benchmark classification problems. We propose here a new ensemble, “Double SVMBagging”, which is a variant of double bagging. In this ensemble method we used the support vector machine as the additional classifiers, built on the out-of-bag...