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Introduction to Numerical and Categorical Data
... temporal and multidimensional data are being collected from many different application fields such as business statistics, demographics, healthcare, biology, chemistry, energy, environment etc. A major challenge today is not to gather data, but to extract meaningful information and gain insights and ...
... temporal and multidimensional data are being collected from many different application fields such as business statistics, demographics, healthcare, biology, chemistry, energy, environment etc. A major challenge today is not to gather data, but to extract meaningful information and gain insights and ...
Data mining and Data warehousing
... Cross validation with separate test data Statistical bounds: use all data for training but apply statistical test to decide right size. (cross-validation dataset may be used to threshold) Use some criteria function to choose best size ...
... Cross validation with separate test data Statistical bounds: use all data for training but apply statistical test to decide right size. (cross-validation dataset may be used to threshold) Use some criteria function to choose best size ...
MEBI 591C/598 – Data and Text Mining in Biomedical Informatics
... mapping low-level data (which are typically too voluminous to understand and digest easily) into other forms that might be ...
... mapping low-level data (which are typically too voluminous to understand and digest easily) into other forms that might be ...
Text mining
... Figure 4: Ten-fold cross-validation accuracy of nearest neighbor classification and various dimensionality reduction methods on (a) movie review polarity data, and (b) sentence polarity data two datasets are shown in Fig. 4. Accuracy is averaged over 10 cross-validation folds, with the folds of the ...
... Figure 4: Ten-fold cross-validation accuracy of nearest neighbor classification and various dimensionality reduction methods on (a) movie review polarity data, and (b) sentence polarity data two datasets are shown in Fig. 4. Accuracy is averaged over 10 cross-validation folds, with the folds of the ...
Lecture 13
... - Unsupervised Learning - Self-organized maps (SOM) - Neural network based method - Originally used as a visualization method for visualize (embedding) high-dimensional data - Also related vector quantization - The idea is to map close data points to the same discrete level ...
... - Unsupervised Learning - Self-organized maps (SOM) - Neural network based method - Originally used as a visualization method for visualize (embedding) high-dimensional data - Also related vector quantization - The idea is to map close data points to the same discrete level ...
Customer Relationship Management Based on Decision Tree
... customer classification and prediction, by which a ...
... customer classification and prediction, by which a ...
Classification using Association Rule Mining
... constructs a highly compact data structure (FP-tree) to compress the original transaction database. It focuses on the frequent pattern (fragment) growth and eliminate repeated database scan. The performance study by Han’s group shows that FP-growth is more efficient than Apriori algorithm. ...
... constructs a highly compact data structure (FP-tree) to compress the original transaction database. It focuses on the frequent pattern (fragment) growth and eliminate repeated database scan. The performance study by Han’s group shows that FP-growth is more efficient than Apriori algorithm. ...
Learning to Improve Area-Under-FROC for Imbalanced Medical
... where (xi,yi) is an instance-label pair in the training data, Ф is a function that maps input data into a higher dimensional space and C+, C- are weights of training errors with respect to the positive and negative examples, respectively. In this task, we set C+ to be 163 times larger than C-. The ...
... where (xi,yi) is an instance-label pair in the training data, Ф is a function that maps input data into a higher dimensional space and C+, C- are weights of training errors with respect to the positive and negative examples, respectively. In this task, we set C+ to be 163 times larger than C-. The ...
Classification and Prediction
... The generated tree may overfit the training data Too many branches, some may reflect anomalies due to noise or outliers Result is in poor accuracy for unseen samples Two approaches to avoid overfitting Prepruning: Halt tree construction early—do not split a node if this would result in the goo ...
... The generated tree may overfit the training data Too many branches, some may reflect anomalies due to noise or outliers Result is in poor accuracy for unseen samples Two approaches to avoid overfitting Prepruning: Halt tree construction early—do not split a node if this would result in the goo ...
Data
... More on Cross-Validation • Standard method for evaluation: stratified 10-fold crossvalidation. • Why 10? Extensive experiments have shown that this is the best choice to get an accurate estimate. • Stratification reduces the estimate’s variance. • Even better: repeated stratified cross-validation: ...
... More on Cross-Validation • Standard method for evaluation: stratified 10-fold crossvalidation. • Why 10? Extensive experiments have shown that this is the best choice to get an accurate estimate. • Stratification reduces the estimate’s variance. • Even better: repeated stratified cross-validation: ...
Neural Network Algorithm - QLD SQL Server User Group
... • Multilayer Perceptron Network = • Back-Propagated Delta Rule Network • Assign weights: assess importance of input on output using training dataset • Batch Learning – Start at outputs and propagate back through the network: – Evaluate weight accuracy: predicted value vs. holdout value – Adjust wei ...
... • Multilayer Perceptron Network = • Back-Propagated Delta Rule Network • Assign weights: assess importance of input on output using training dataset • Batch Learning – Start at outputs and propagate back through the network: – Evaluate weight accuracy: predicted value vs. holdout value – Adjust wei ...
R-DataVisualization(II)
... • Use I(value) to indicate a specific value. For example size=z makes the size of the plotted points or lines proportional to the values of a variable z. In contrast, size=I(3) sets each point or line to three times the default size. ...
... • Use I(value) to indicate a specific value. For example size=z makes the size of the plotted points or lines proportional to the values of a variable z. In contrast, size=I(3) sets each point or line to three times the default size. ...
Similarly, the data in the relational format can be represented as P
... we use a higher k? For example using k = 5 instead of k = 3. The answer is if there are too few points (for example only one or two points) on the boundary to make k neighbors in the neighborhood, we have to expand neighborhood and include some not so similar points which will decrease the classific ...
... we use a higher k? For example using k = 5 instead of k = 3. The answer is if there are too few points (for example only one or two points) on the boundary to make k neighbors in the neighborhood, we have to expand neighborhood and include some not so similar points which will decrease the classific ...
Data mining - Department of Computer Science and Engineering
... Tree is constructed in a top-down recursive manner At start, all the training examples are at the root Examples are partitioned recursively based on selected attributes Attributes are selected on the basis of an impurity function (e.g., ...
... Tree is constructed in a top-down recursive manner At start, all the training examples are at the root Examples are partitioned recursively based on selected attributes Attributes are selected on the basis of an impurity function (e.g., ...
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.