Applications of data mining techniques in improving the
... IV, IKONOS or CARTOSAT, showing a diversity of land cover forms, the traditional algorithms have not been found adequate to obtain satisfactory results, because of high variance in the data. In high resolution images, spatial neighbouring pixels are highly correlated and in the most instances, they ...
... IV, IKONOS or CARTOSAT, showing a diversity of land cover forms, the traditional algorithms have not been found adequate to obtain satisfactory results, because of high variance in the data. In high resolution images, spatial neighbouring pixels are highly correlated and in the most instances, they ...
SYLLABUS COURSE TITLE STATISTICAL LEARNING
... - induce classification and decision tree and classify new cases FINAL COURSE OUTPUT - SOCIAL COMPETENCES COURSE ORGANISATION –LEARNING FORMAT AND NUMBER OF HOURS ...
... - induce classification and decision tree and classify new cases FINAL COURSE OUTPUT - SOCIAL COMPETENCES COURSE ORGANISATION –LEARNING FORMAT AND NUMBER OF HOURS ...
On Reducing Classifier Granularity in Mining Concept
... When record ri w arrives, insert it into the REC-tree and update the sup. and conf. of the rules matched by it. Delete oldest record and update the value matched by it. ...
... When record ri w arrives, insert it into the REC-tree and update the sup. and conf. of the rules matched by it. Delete oldest record and update the value matched by it. ...
E-Governance in Elections: Implementation of Efficient Decision
... available dataset. This system assists to reduce misclassification error pruning. For each leaf in a tree, it is possible to make a number of instances which are misclassified on a training set by propagating errors from. It can be compared with error rate if the leaf was changed by the most common ...
... available dataset. This system assists to reduce misclassification error pruning. For each leaf in a tree, it is possible to make a number of instances which are misclassified on a training set by propagating errors from. It can be compared with error rate if the leaf was changed by the most common ...
Poster Session 121312
... Our initial attempts to analyze the data occurred primarily in MATLAB. Because the data was categorized into two labels, good or bad car purchases, we used logistic regression and libLINEAR1 v.1.92. Initial attempts at classification went poorly due to heavy overlap between our good and bad training ...
... Our initial attempts to analyze the data occurred primarily in MATLAB. Because the data was categorized into two labels, good or bad car purchases, we used logistic regression and libLINEAR1 v.1.92. Initial attempts at classification went poorly due to heavy overlap between our good and bad training ...
A large number of problems in data mining are related to fraud
... Train with the two neural network methods (multilayer perceptron and radial basis functions) and the KNN and Bayesian Network methods by changing the parameters such as hidden nodes in each layer with target variable as Fraud Prediction –Yes/No ( Flag). This is an imbalanced dataset. Fraud cases are ...
... Train with the two neural network methods (multilayer perceptron and radial basis functions) and the KNN and Bayesian Network methods by changing the parameters such as hidden nodes in each layer with target variable as Fraud Prediction –Yes/No ( Flag). This is an imbalanced dataset. Fraud cases are ...
Learning from Imbalanced Data Sets with Boosting and Data
... Provide a concept for supervised clustering with target class. An alternative method in handling data with mixed type. Attempt to represent hyperspace via a line concept. If the target class is the determinant of clustering, why we need the redistribution to improve the robustness? ...
... Provide a concept for supervised clustering with target class. An alternative method in handling data with mixed type. Attempt to represent hyperspace via a line concept. If the target class is the determinant of clustering, why we need the redistribution to improve the robustness? ...
shattered - Data Science and Machine Intelligence Lab
... pac Model Key assumption: Training and testing data are generated i.i.d. according to an fixed but unknown distribution D When we evaluate the “quality” of a hypothesis (classification function) h 2 H we should take the unknown distribution D into account ( i.e. “average error” or “expected erro ...
... pac Model Key assumption: Training and testing data are generated i.i.d. according to an fixed but unknown distribution D When we evaluate the “quality” of a hypothesis (classification function) h 2 H we should take the unknown distribution D into account ( i.e. “average error” or “expected erro ...
Review Questions for September 23
... Assume we have a dataset in which the median of the first attribute is twice as large as the mean of the first attribute? What does this tell you about the distribution of the first attribute? What is (are) the characteristic(s) of a good histogram (for an attribute)? Assume you find out that two at ...
... Assume we have a dataset in which the median of the first attribute is twice as large as the mean of the first attribute? What does this tell you about the distribution of the first attribute? What is (are) the characteristic(s) of a good histogram (for an attribute)? Assume you find out that two at ...
LOYOLA COLLEGE (AUTONOMOUS), CHENNAI – 600 034
... 13 a) Illustrate the K-means clustering algorithm to cluster the following items {2,4,10,12,3,20,30,11,25} when k =2 (Or) b) With a neat diagram, explain the use of Dendrograms in clustering. 14 a) Discuss how sampling algorithm is suitable for handling large databases (Or) b) Describe how data par ...
... 13 a) Illustrate the K-means clustering algorithm to cluster the following items {2,4,10,12,3,20,30,11,25} when k =2 (Or) b) With a neat diagram, explain the use of Dendrograms in clustering. 14 a) Discuss how sampling algorithm is suitable for handling large databases (Or) b) Describe how data par ...
Classification_Feigelson
... Algorithms include ID3, C4.5 an Random Forests. k-Nearest Neighbor (k-NN) classifiers are very simple and computationally efficient: choose a distance metric and integer k; locate the k nearest neighbors of the training set for each member of the test set, test set point class is set to be the most ...
... Algorithms include ID3, C4.5 an Random Forests. k-Nearest Neighbor (k-NN) classifiers are very simple and computationally efficient: choose a distance metric and integer k; locate the k nearest neighbors of the training set for each member of the test set, test set point class is set to be the most ...
Human Gesture Recognition Using Kinect Camera
... distance settings (2m and 3m) ◦ 1,200 vectors for both camera distance settings (2m and 3m) ◦ The output data contain 3,600 vectors in total ...
... distance settings (2m and 3m) ◦ 1,200 vectors for both camera distance settings (2m and 3m) ◦ The output data contain 3,600 vectors in total ...
Algorithms
... Systems that construct a classifier • input: a collection of cases – Each belong to one of small number of classes – Described by values for fixed number of attributes ...
... Systems that construct a classifier • input: a collection of cases – Each belong to one of small number of classes – Described by values for fixed number of attributes ...
K-nearest neighbors algorithm
In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.Both for classification and regression, it can be useful to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.A shortcoming of the k-NN algorithm is that it is sensitive to the local structure of the data. The algorithm has nothing to do with and is not to be confused with k-means, another popular machine learning technique.