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What is Bayesian Classifier and how Bayesian Classifier works ?

Bayesian classifier: introduction A Bayesian classifier is based on idea that can predict values of features for members of learned classes. In data set patterns are grouped in classes because they have common features. Such classes are often called natural kinds. The idea behind a Bayesian classifier is that, if someone knows the classes, it can predict values of  similar other patterns. If it does not know the class, Bayes’ rule can be used to predict the class given according to attributes. In a Bayesian classifier, the learning model is a probabilistic model of attributes and that predict the classification labels of a new similar Pattern. A latent variable is a probabilistic variable that is not observed. A Bayesian classifier is a probabilistic model where the classification is a latent variable that is probabilistically related to the observed variables. Classification then becomes inference in the probabilistic model. Naive Bayes is a family of probabilistic algorithms that take advantage of probability theory and Bayes’ Theorem to predict the category of a sample (like a piece of news or a customer review). They are probabilistic, which means that they calculate the probability of each category for a given sample, and then output the category with the highest one. The…

An Overview of Particle swarm optimization (PSO)

Particle swarm optimization (PSO): introduction Particle swarm optimization (PSO) simulates the behaviors of bird flocking. Suppose the following scenario: a group of birds are randomly searching food in an area. There is only one piece of food in the area being searched. All the birds do not know where the food is. But they know how far the food is in each iteration. So what’s the best strategy to find the food? The effective one is to follow the bird which is nearest to the food. Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995. Definition Theory of particle swarm optimization (PSO) has been growing rapidly. PSO has been used by many applications of several problems. The algorithm of PSO emulates from behavior of animals societies that don’t have any leader in their group or swarm, such as bird flocking and fish schooling. Typically, a flock of animals that have no leaders will find food by random, follow one of the members of the group that has the closest position with a food source (potential solution). The flocks achieve their best condition simultaneously through communication among members who already have…

Support Vector Machine (SVM)

In recent years, the Artificial Neural Networks (ANNs) have been playing a significant role for variants of data mining tasks which is extensively popular and active research area among the researchers. To intend of neural network is to mimic the human ability to acclimatize to varying circumstances and the current environment. The subtle use of Support Vector Machine (SVM) in various data mining applications makes it an obligatory tool in the development of products that have implications for the human society. SVMs, being computationally powerful tools for supervised learning, are widely used in classification, clustering and regression problems. SVMs have been successfully applied to a variety of real-world problems like particle identification, face recognition, text categorization, bioinformatics, civil engineering and electrical engineering etc. SVM have attracted a great deal of attention in the last decade and actively applied to various domains applications. SVMs are typically used for learning classification, regression or ranking function. SVM are based on statistical learning theory and structural risk minimization principal and have the aim of determining the location of decision boundaries also known as hyperplane that produce the optimal separation of classes. Maximizing the margin and thereby creating the largest possible distance between the separating…

Neural Network

Neural Network Basics An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. Neural Network  Definition Work on artificial neural networks, commonly referred to as “neural networks,” has been motivated right from its inception by the recognition that the human brain computes in an entirely different way from the conventional digital computer. The brain is a highly complex, nonlinear, and parallel computer (information-processing system). It has the capability to organize its structural constituents, known as neurons, so as to perform certain computations (e.g., pattern recognition, perception, and motor control) many times faster than the fastest digital computer in existence today. Consider, for example,…

Hidden Markov Model

Hidden Markov Model (HMM) The Hidden Markov Model (HMM) is a powerful statistical tool for modeling generative sequences that can be characterized by an underlying process generating an observable sequence. A hidden Markov model is a doubly stochastic process, with an underlying stochastic process that is not observable (hence the word hidden), but can be observed through another stochastic process that produces the sequence of observations. The hidden process consists of a set of states connected to each other by transitions with probabilities, while the observed process consists of a set of outputs or observations, each of which may be emitted by each state according to some probability density function (pdf). Depending on the nature of this pdf, several HMM classes can be distinguished. If the observations are naturally discrete or quantized using vector quantization. The Hidden Markov Model is a finite set of states, each of which is associated with a (generally multidimensional) probability distribution Transitions among the states are governed by a set of probabilities called transition probabilities. In a particular state an outcome or observation can be generated, according to the associated probability distribution. It is only the outcome, not the state visible to an external observer…

Decision Trees Applications

Decision Tree Overview In data mining techniques two kinds of basic learning processes are available namely supervised and unsupervised. when we talk about the supervised learning techniques the decision tree learning is one of the most essential technique of classification and prediction. A number of different kinds of decision tree algorithms are available i.e. ID3, C4.5, C5.0, CART, SLIQ and others. All these algorithms the used to generate the transparent data models. these data models can be evaluated using the paper and pencil. therefore that is an effective data modeling technique. Applications of Decision Trees Application of Decision Tree Algorithm in Healthcare Operations [1]: the decision trees are used to visualize the data patterns in form of tree data structure. that help to also prepare the relationship among the attributes and the final class labels. thus the patient’s different health attributes can help to understand the symptoms and possibility by comparing the historical data available with the similar attributes. Manufacturing and Production: in a large production based industries where the regulation of production and planning is required. the decision tree models helps for understanding the amount of production, time of production and other scenarios. that can be evaluated using the past scenarios of…

ID3 Decision Tree in Data Mining

ID3 Decision Tree Overview Engineered by Ross Quinlan the ID3 is a straightforward decision tree learning algorithm. The main concept of this algorithm is construction of the decision tree through implementing a top-down, greedy search by the provided sets for testing every attribute at each node of decision. With the aim of selecting the attribute which is most useful to classify a provided set of data, a metric is introduced named as Information Gain [1]. To acquire the finest way for classification of learning set, one requires to act for minimizing the fired question (i.e. to minimize depth of the tree). Hence, some functions are needed that is capable of determine which questions will offer the generally unbiased splitting. One such function is information gain metric. Entropy In order to define information gain exactly, we require discussing entropy first. Let’s assume, without loss of simplification, that the resultant decision tree classifies instances into two categories, we’ll call them ​\( [ P_{positive} ] and [ N_{negative} ] \)​ Given a set S, containing these positive and negative targets, the entropy of S related to this Boolean classification is: ​\( [ P_{positive} ] \)​: proportion of positive examples in S ​\( [ N_{negative} ]…

k Nearest Neighbor (KNN)

k Nearest Neighbor (KNN): introduction The necessity of data mining techniques has emerged quite immensely nowadays due to massive increase in data. Data mining is the process of extracting patterns and mining knowledge from data. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions). KNN has been used in statistical estimation and pattern recognition already in the beginning of 1970’s as a non-parametric technique. The model for KNN is the entire training dataset. When a prediction is required for a unseen data instance, the KNN algorithm will search through the training dataset for the k-most similar instances. The prediction attribute of the most similar instances is summarized and returned as the prediction for the unseen instance. Nearest neighbor classifiers is a lazy learner’s method and is based on learning by analogy. It is a supervised classification technique which is used widely. Unlike the previously described methods the nearest neighbor method waits until the last minute before doing any model construction on a given tuple. In this method the training tuples are represented in N-dimensional space. When given an unknown tuple, k-nearest neighbor classifier searches the k…

Introduction of Decision Trees

Decision Tree: Overview Data mining techniques that help to make decisions using the available facts can be termed as Decision Tree. Decision trees are not only useful for decision-making applications it is also used for classification and prediction task. There are some popular decision Tree algorithms namely C4.5, ID3, and CART. These algorithms are supervised learning algorithms. During training, the input samples are represented as a tree data structure. example An example of a decision tree is given in figure 1, where nodes of the tree show the attributes of the data set. Additionally, edges create a relationship among two nodes using the values of available attributes. The leaf node of the tree is recognized as the decision node. Figure 1 decision tree example Figure 1, demonstrates a decisions tree. Here decision labels are (yes or no), it is also known as class labels. In decision trees, the class label is placed on leaf nodes. Additionally, the nodes humidity, outlook, and wind are attributes in data-set. Because the data set contains decision and attributes and decision tree graphically represents the data. Therefore it is helpful to understand the relationship between them. Sometimes the decision trees are used in form of IF…

Web recommendation system

Web recommendation system: Introduction The term recommendation is used for describing the suggestions of a particular product or service. therefore the web recommendation systems are a essential part of e-commerce applications. The users who search about some kinds of product or services the recommendation systems helps them by suggesting the most appropriate product or services. In most of the cases the web based recommendation systems are developed using the web usage mining and content mining techniques.  In this context using this concept a number of applications  are created. The recommendations systems can be described in three major categories. There is an extensive class of Web applications that involve predicting user responses to options. Such a facility is called a recommendation system. However, to bring the problem into focus, two good examples of recommendation systems are [1]: Offering news articles to on-line newspaper readers, based on a prediction of reader interests. Offering customers of on-line retailer suggestions about what they might like to buy, based on their past history of purchases and/or product searches. Recommendation systems use a number of different technologies. That can be classify these systems into two broad groups Content-based systems examine properties of the items recommended. For instance, if…