Other Methods

SVM (Support Vector Machine)

In this algorithm, each data item is plotted as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate.

The aim is to determine the location of decision boundaries also known as hyperplane that produce the optimal separation of classes. Multiple frontiers are produced to suit the data. The best frontier is the one which is farthest away from the nearest support vector.

In case the data cannot be clearly segregated, the data is transformed to a higher dimensional plane where it can be segregated easily.

Advantages : Robust, accurate classification

Disadvantages : Computationally expensive 

Naïve Bayes

It is a classification technique based on Bayes’ theorem with an assumption of independence between predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

P(c|x) is the posterior probability of class (target) given predictor (attribute). 

P(c) is the prior probability of class. 

P(x|c) is the likelihood which is the probability of predictor given class. 

P(x) is the prior probability of predictor.

1. Convert the data set to frequency table
2. Create Likelihood table by finding the probabilities
3. Now, use Naive Bayesian equation to calculate the posterior probability for each class. The class with the highest posterior probability is the outcome of prediction.

Naive Bayes predicts the probability of different class based on various attributes. This algorithm is mostly used in text classification and with problems having multiple classes.

Artificial Neural Network (ANN)

Artificial neural networks (ANNs) are types of computer architecture inspired by biological neural networks (Nervous systems of the brain) and are used to approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are presented as systems of interconnected “neurons” which can compute values from inputs and are capable of machine learning as well as pattern recognition due their adaptive nature

An artificial neural network operates by creating connections between many different processing elements each corresponding to a single neuron in a biological brain.

Implemented on a single computer, an artificial neural network is normally slower than more traditional solutions of algorithms. The ANN’s parallel nature allows it to be built using multiple processors giving it a great speed advantage at very little development cost.