A Survey on Pre-processing and Post-processing
... bins [3]. The value in each bin is smooth out by substituting it with the mean or median value of this bin. Bin boundaries, the minimum and maximum value in each bin, are also used to smooth out the bin value. Each bin value is substituted by closest boundary value. Clustering is used to detect the ...
... bins [3]. The value in each bin is smooth out by substituting it with the mean or median value of this bin. Bin boundaries, the minimum and maximum value in each bin, are also used to smooth out the bin value. Each bin value is substituted by closest boundary value. Clustering is used to detect the ...
Workload-Aware Anonymization Techniques for Large
... describe two techniques for scaling our proposed algorithms to datasets much larger than main memory. An experimental performance evaluation in Section 6 indicates the practicality of these techniques. The article concludes with discussions of related and future work in Sections 7 and 8. 2. PRELIMIN ...
... describe two techniques for scaling our proposed algorithms to datasets much larger than main memory. An experimental performance evaluation in Section 6 indicates the practicality of these techniques. The article concludes with discussions of related and future work in Sections 7 and 8. 2. PRELIMIN ...
RSVM: Reduced Support Vector Machines
... The motivation for RSVM comes from the practical objective of generating a nonlinear separating surface (9) for a large dataset which requires a small portion of the dataset for its characterization. The difficulty in using nonlinear kernels on large datasets is twofold. First is the computational d ...
... The motivation for RSVM comes from the practical objective of generating a nonlinear separating surface (9) for a large dataset which requires a small portion of the dataset for its characterization. The difficulty in using nonlinear kernels on large datasets is twofold. First is the computational d ...
Health Monitoring for Elderly: An Application Using Case
... The personalized health profiling approach is a four-session approach in 9-minute duration, and the sessions are baseline, deep breath, activity, and relax. The details of the personalized health profiling approach can be found in [3]. The “WristOx2” sensor is connected as shown in Figure 1 and send ...
... The personalized health profiling approach is a four-session approach in 9-minute duration, and the sessions are baseline, deep breath, activity, and relax. The details of the personalized health profiling approach can be found in [3]. The “WristOx2” sensor is connected as shown in Figure 1 and send ...
A Parallel Attribute Reduction Algorithm based on Affinity
... application fields and cross-cutting features with other research direction. As an unsupervised machine learning method, cluster analysis has been widely used in natural and social science. It classifies some objects into several clusters, making the differences of the objects in distinct classes as ...
... application fields and cross-cutting features with other research direction. As an unsupervised machine learning method, cluster analysis has been widely used in natural and social science. It classifies some objects into several clusters, making the differences of the objects in distinct classes as ...
pptx
... – A query example (vector) q comes – Find closest example(s) x* – Predict y* • Works both for regression and classification – Collaborative filtering is an example of kNN classifier • Find k most similar people to user x that have rated movie y • Predict rating yx of x as an average of yk J. Leskove ...
... – A query example (vector) q comes – Find closest example(s) x* – Predict y* • Works both for regression and classification – Collaborative filtering is an example of kNN classifier • Find k most similar people to user x that have rated movie y • Predict rating yx of x as an average of yk J. Leskove ...
Mining Partial Periodicity in Large Time Series Databases using
... In this excerpt from the data file, the first row has been added by the discretization program to fill in the sequence of days, since otherwise the segments would start on day 2 (Tuesday). The first column contains the actual date included for reference, and in the case of added rows is repeated bas ...
... In this excerpt from the data file, the first row has been added by the discretization program to fill in the sequence of days, since otherwise the segments would start on day 2 (Tuesday). The first column contains the actual date included for reference, and in the case of added rows is repeated bas ...
Software Defect Prediction Based on Classification Rule
... There has been a huge growth in the demand for software quality during recent ages. As a consequence, issues are related to testing, becoming increasingly critical. The ability to measure software defect can be extremely important for minimizing cost and improving the overall effectiveness of the te ...
... There has been a huge growth in the demand for software quality during recent ages. As a consequence, issues are related to testing, becoming increasingly critical. The ability to measure software defect can be extremely important for minimizing cost and improving the overall effectiveness of the te ...
Stock Market Prediction using Social Media Analysis
... Machine learning has strong connections with statistical and mathematical optimization, whereas all of these areas aim at locating interesting regularities, patterns and concepts from empirical data. Therefore, statistics and mathematical optimization provide methods and applications to the area of ...
... Machine learning has strong connections with statistical and mathematical optimization, whereas all of these areas aim at locating interesting regularities, patterns and concepts from empirical data. Therefore, statistics and mathematical optimization provide methods and applications to the area of ...
Data Mining and Knowledge Discovery Practice notes: Numeric
... Consider a dataset with a target variable with five possible values: ...
... Consider a dataset with a target variable with five possible values: ...
Comprehensible Classification Models
... rule induction algorithm. However, this would still leave us with the problem of how to estimate the relevance of an attribute in the entire classification model (with all rules). One solution is the same type of attribute relevance measure proposed for decision trees in Subsection 3.1, I.e., the re ...
... rule induction algorithm. However, this would still leave us with the problem of how to estimate the relevance of an attribute in the entire classification model (with all rules). One solution is the same type of attribute relevance measure proposed for decision trees in Subsection 3.1, I.e., the re ...
Data Mining: Concepts and Techniques
... Estimate accuracy of the model The known label of test sample is compared with the classified result from the model Accuracy rate is the percentage of test set samples that are correctly classified by the model Test set is independent of training set, otherwise over-fitting ...
... Estimate accuracy of the model The known label of test sample is compared with the classified result from the model Accuracy rate is the percentage of test set samples that are correctly classified by the model Test set is independent of training set, otherwise over-fitting ...
BlueBRIDGE Competitive Call – Data management services for
... as a maps or datasets in the eInfrastructure. NetCDF-CF files are encouraged, as WMS and WCS maps will be produced using this format. For other types of files (GeoTiffs, ASC etc.) only the raw datasets will be published. The resulting map or dataset will be accessible via the VRE GeoExplorer by the ...
... as a maps or datasets in the eInfrastructure. NetCDF-CF files are encouraged, as WMS and WCS maps will be produced using this format. For other types of files (GeoTiffs, ASC etc.) only the raw datasets will be published. The resulting map or dataset will be accessible via the VRE GeoExplorer by the ...
Data Mining: Concepts and Techniques
... Model construction: describing a set of predetermined classes n Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute n The set of tuples used for model construction is training set n The model is represented as classification rules, decision ...
... Model construction: describing a set of predetermined classes n Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute n The set of tuples used for model construction is training set n The model is represented as classification rules, decision ...
Pattern Analysis & Machine Intelligence Research Group
... scalability of clustering algorithms to large volumes of data objects, and enhancing the robustness by reducing the sensitivity to outlier data objects or noisy attributes. ...
... scalability of clustering algorithms to large volumes of data objects, and enhancing the robustness by reducing the sensitivity to outlier data objects or noisy attributes. ...
A Comparison of the Discretization Approach for CST and
... the attribute into intervals [4],[5]. Discretization makes learning more accurate and faster. Discretization as used in this paper and in the machine learning literature in general is a process of transforming a continuous attribute value into a finite number of intervals and associating each interv ...
... the attribute into intervals [4],[5]. Discretization makes learning more accurate and faster. Discretization as used in this paper and in the machine learning literature in general is a process of transforming a continuous attribute value into a finite number of intervals and associating each interv ...
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.