Segmentation of Machine learning Algorithm
- 1. Classification types S.No. Classification Basedon Types Algorithm used 1 Training sample based Supervised Classification Parallel pipelined algorithm, maximum likelihood algorithm, minimum distance to mean algorithm Unsupervised classification K means clustering algorithm 2 Parameter based Parametric classifier Maximum likelihood algorithm, linear discriminant analysis Non parametric classifier Artificial Neural Network(ANN), Support vector machine (SVM), Decision tree classifier 3 Pixel based Per pixel classifier Maximum likelihood algorithm, Artificial Neural Network(ANN), Support vector machine (SVM) Sub pixel classifier Spectral mixture analysis, sub pixel classifier, fuzzy set classifier Per-field classifier GIS based classification approaches Object oriented eCognition 4 Spatial element Hard classification maximum likelihood algorithm, minimum distance, Artificial Neural Network(ANN), Support vector machine (SVM), Decision tree algorithm Soft classification Fuzzy algorithm 5 Spatial information Spectral classifier maximum likelihood algorithm, minimum distance, Artificial Neural Network(ANN) Contextual classifier Frequency-based contextual classifier Spectral – contextual classifier Combination of parametric or non-parametric and contextual algorithm 6 Multiple classifier Voting rules, Bayesian formalism, evidential reasoning, multiple neural network.
- 2. Classification Techniques Classification method Description Characteristics Artificial Neural network ANN is a type of artificial intelligence that imitates some functions of the person mind. ANN has a normal tendency for storing experiential knowledge. An ANN consists of a sequence of layers, each layer consists of a set of neurones. All neurones of every layer are linked by weighted connections to all neurones on the preceding and succeeding layers It uses Nonparametric approach. Performance and accuracy depends upon the network structure and number of inputs Decision tree DT calculates class membership by repeatedly partitioning a dataset into uniform subsets Hierarchical classifier permits the acceptations and rejection of class labels at each intermediary stage. This method consists of 3 parts: Partitioning the nodes, find the terminal nodes and allocation of class label to terminal nodes DT are based on hierarchical rule based method and use Nonparametric approach. Support Vector Machine A support vector machine builds a hyper plane or set of hyper planes in a high- or infinite dimensional space, used for classification. Good separation is achieved by the hyper plane that has the largest distance to the nearest training data point of any class (functional margin), generally larger the margin lower the generalization error of the classifier. SVM uses Nonparametric with binary classifier approach and can handle more input data very efficiently. Performance and accuracy depends upon the hyper plane selection and kernel parameter.