Gesture Recognition from Data Streams of Human Motion Sensor Using Accelerated PSO Swarm Search Feature Selection Algorithm

Table 2

Classification algorithms used in experiments.

Traditional classifier	Description
HyperPipe (HP)	For each category a HyperPipe is constructed that contains all points of that category (essentially records the attribute bounds observed for each category). Test instances are classified according to the category that “most contains the instance”
Naviie Bayes (NB)	Naive Bayes classifier using estimator classes. Numeric estimator precision values are chosen based on analysis of the training data. For this reason, the classifier is not an Updateable Classifier (which in typical usage is initialized with zero training instances)
BayesNet (BN)	Bayes network learning using various search algorithms and quality measures.; Base class for a Bayes network classifierprovides data structures (network structure, conditional probability distributions, etc.) and facilities common to Bayes Network learning algorithms like and
Decision tree (DT)	Generating a pruned C4.5 decision tree
Random forest (RF)	Constructing a forest of random trees
Support vector machine (SVM)	A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier)
Neural network (NN)	A classifier that uses backpropagation to classify instances. The nodes in this network are all sigmoid (except for when the class is numeric in which case the output nodes become unthresholded linear units)
Incremental classifier	Description
Random Hoeffding tree (RHT)	Random decision trees for data streams
Hoeffding tree (HT)	Very fast decision tree implementation using Hoeffding bound
Hoeffding option tree (HOT)	Hoeffding tree: Single tree that represents multiple trees
NBUpdateable (NBup)	This is the updateable version of Naive Bayes. This classifier will use a default precision of 0.1 for numeric attributes when buildClassifier is called with zero training instances
Active (AC)	Active learning classifier for evolving data streams
Adwin (AW)	ADaptive sliding WINdow method. This method is a change detector and estimator. It keeps a variable-length window of recently seen items, with the property that the window has the maximal length statistically consistent with the hypothesis “there has been no change in the average value inside the window”
Kstar	is an instance-based classifier; that is, the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function