BAOA (Big Data Online Analysis)
BAOA is the most popular for data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
BAOA (Big Data On-line Analysis) is a framework for data stream mining. It includes tools for evaluation and a collection of machine learning algorithms. The goal of BAOA is a benchmark framework for running experiments in the data stream mining context by proving
- storable settings for data streams (real and synthetic) for repeatable experiments
- a set of existing algorithms and measures form the literature for comparison and
- an easily extendable framework for new streams, algorithms and evaluation methods.
The workflow in BAOA follows the simple schema depicted below: first a data stream (feed, generator) is chosen and configured, second an algorithm (e.g. a classifier) is chosen and its parameters are set, third the evaluation method or measure is chosen and finally the results are obtained after running the task.
To run an experiment using BAOA, the user can choose between a graphical user interface (GUI) or a command line execution. BAOA currently supports stream classification, stream clustering, outlier detection, change detection and concept drift and recommender systems. We are working on extending BAOA to support other mining tasks on data streams.
Face Detection (Haar object detector)
Face detection using the Face detection based in Haar-like rectangular features method often known as the Viola-Jones method.
Face (or object) tracking using Viola-Jones for face detection and Camshift as the object tracker. Can be used in RGB and HSL color spaces (may require some tuning for HSL).
SURF feature detection
Mouse Gesture Recognition
Learning and recognition of mouse gestures using hidden Markov model-based classifiers and Hidden Conditional Random Fields.
Image Recognition with Neural Networks
In this application, we demonstrate Hough Line and Circle detection, as well as using the Image Processing to detect Triangles and Rectangles in the image.
One-Layer Perceptron Classifier
This sample application is similar to the above one, but it demonstrates classification of more data classes (also all of them are linearly separable from the rest of data). To be able to classify more classes this application uses already a layer of perceptrons, but not a single one. The demonstrated simplest neural network has number of outputs equal to number of classes. For a given input the network sets one of its outputs to 1 and the rest of outputs to 0. The output with value set to 1 represents class of the value given to the network.