User:Jalayer masoud/sandbox

Ensemble learning applications
In the recent years, due to the growing computational power which allows training large ensemble learning in a reasonable time frame, the number of its applications has been increasingly grown. Some of the applications of ensemble classifiers include:

Land cover mapping
Land cover mapping is one of the major applications of Earth observation satellite sensors, using remote sensing and geospatial data, to identify the materials and objects which are located on the surface of target areas. Generally, the classes of target materials include roads, buildings, rivers, lakes, vegetation. Some different ensembles based on artificial neural networks, kernel principal component analysis(KPCA) ,decision trees with Boosting , random forest and approaches for automatic designing of multiple classifier systems.

Change detection
Change detection is an image analysis problem, consisting in the identification of places where the land cover has changed in time. Change detection is widely used in fields such as urban growth, forest and vegetation dynamics, land use and disaster monitoring. The earliest applications of ensemble classifiers in change detection are designed with the majority voting, Bayesian average and the maximum posterior probability.

Distributed denial of service
Distributed denial of service is one of the most threatening cyber-attacks that may happens to an internet service provider. By combining the the output of single classifiers, ensemble classifiers reduce the total error of detecting and discriminating such attacks from legitimate flash crowds.

Malware Detection
Classification of malware codes such as computer viruses, computer worms, trojans, ransomware and spywares with usage of Machine Learning techniques is inspired by the document categorization problem. Ensemble learning systems have showed a proper efficacy in this area.

Intrusion detection
An Intrusion detection system monitors computer network or computer systems to identify intruder codes like an Anomaly detection process. Ensemble learning successfully aids such monitoring systems to reduce their total error.

Face recognition
Face recognition, which recently has become one of the most popular research areas of pattern recognition, copes with identification or verification of a person by his/her digital images. Hierarchial ensembles based on Gabor Fisher classifier and Independent component analysis preprocessing techniques are some of the earliest ensembles employed in this field.

Emotion recognition
However speech recognition is mainly based on Deep Learning, as most of the industry players in this field like Google, Microsoft and IBM revealed that the core technology of their speech recognition is based on it, speech-based emotion recognition can have a satisfactory performance with ensemble learning. It also has being successfully used in Facial Emotion Recognition.

Fraud detection
Fraud detection deals with the identification of bank fraud, such as money laundering, credit card fraud and telecommunication fraud, which have vast domains of research and applications of Machine Learning. Because ensemble learning improves the robustness of the normal behavior modelling, it has been proposed as an efficient technique to detect such fraudulent cases and activities in the banking and credit card systems.

Financial decision-making
The accuracy of prediction of business failure is a very crucial issue in financial decision-making. Therefore some different ensemble classifiers are proposed to predict financial crises and financial distress. Also, in the trade-based manipulation problem, where traders attempt to manipulate stock prices by buying and selling activities, ensemble classifiers are required to use the changes in the stock market data itself and then detect suspicious symptom of stock price manipulation.

Medicine
Ensemble classifiers have been successfully applied to Neuroscience, Proteomics and medical diagnosis. Like in neuro-cognitive disorder (i.e. Alzheimer or myotonic dystrophy) detection based on MRI datasets