dna sequence, containing all genetic traits is not a functional entity. instead, it transfers to protein sequences by transcription and translation processes. this protein sequence takes on a 3d structure later, which is a functional unit and can manage biological interactions using the information encoded in dna. every life process one can figure is undertaken by proteins with specific functio...

One of the most important issues concerning the sensor data in the Wireless Sensor Networks (WSNs) is the unexpected data which are acquired from the sensors. Today, there are numerous approaches for detecting anomalies in the WSNs, most of which are based on machine learning methods. In this research, we present a heuristic method based on the concept of “ensemble of classifiers” of data minin...

In ensemble teacher learning, ensemble teachers have only uncertain information about the true teacher, and this information is given by an ensemble consisting of an infinite number of ensemble teachers whose variety is sufficiently rich. In this learning, a student learns from an ensemble teacher that is iteratively selected randomly from a pool of many ensemble teachers. An interesting point ...

A concise overview of the fundamentals and the main types of machine ensembles serves to propose a structured perspective for the papers that are included in this special session. The subsequent brief discussion of the works, emphasizing their principal contributions, permits an extraction of a series of suggestions for further research in the fruitful area of ensemble learning.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

This commutation is about training the Xception model for the Kaggle competition “Cdiscount’s Image Classification Challenge”. The paper will briefly describe all methods/code (github.com/ardiloot/CDiscountClassifier) used to train the model for best classification performance. Mainly, the effect of the augmentation (both train and test time) and algebraic ensemble methods were studied. In the ...

Several works showed that biomolecular data integration is a key issue to improve the prediction of gene functions. Quite surprisingly only little attention has been devoted to data integration for gene function prediction through ensemble methods. In this work we show that relatively simple ensemble methods are competitive and in some cases are also able to outperform state-of-the-art data int...