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Abstract - CSEPACK
... predetermined distributions, and this type of approach aims to find the outliers which deviate form such distributions. For distance-based methods, the distances between each data point of interest and its neighbors are calculated. If the result is above some predetermined threshold, the target inst ...
... predetermined distributions, and this type of approach aims to find the outliers which deviate form such distributions. For distance-based methods, the distances between each data point of interest and its neighbors are calculated. If the result is above some predetermined threshold, the target inst ...
A Comparison between Preprocessing Techniques - CEUR
... In the executed tests, the features collected have a minimum presence in the text that is greater than or equal to 5. The Ngrams used are only one-grams and bi-grams. Before starting the simulation with the test set, a 10-fold crossvalidation is carried out. In particular, we searched for the optima ...
... In the executed tests, the features collected have a minimum presence in the text that is greater than or equal to 5. The Ngrams used are only one-grams and bi-grams. Before starting the simulation with the test set, a 10-fold crossvalidation is carried out. In particular, we searched for the optima ...
an integrated approach for supervised learning
... visitors and guests on products enables the organizations to improve their marketing strategy. It has increased big e-commerce sites and recommendations of products and services sites. The large number of reviews on a product promotes easy access to useful and reasonable information to visitors. It ...
... visitors and guests on products enables the organizations to improve their marketing strategy. It has increased big e-commerce sites and recommendations of products and services sites. The large number of reviews on a product promotes easy access to useful and reasonable information to visitors. It ...
Impact of attribute selection on the accuracy of
... affects the accuracy of classification techniques. Attribute Selection is a domain in Data Mining for selecting a subset of relevant attributes. It removes redundant or irrelevant attributes from the dataset. Through this proposed work we indent to analyze the impact of some most commonly used featu ...
... affects the accuracy of classification techniques. Attribute Selection is a domain in Data Mining for selecting a subset of relevant attributes. It removes redundant or irrelevant attributes from the dataset. Through this proposed work we indent to analyze the impact of some most commonly used featu ...
Mid1-16-sol - Department of Computer Science
... c) gpa (which is a real number with mean 2.8 standard deviation is 0.8, and maximum 4.0 and minimum 2.1) d) gender is an nominal attribute taking values in {male, female}. Assume that the attributes qud and gpa are of major importance and the attribute gender is of a minor importance when assessing ...
... c) gpa (which is a real number with mean 2.8 standard deviation is 0.8, and maximum 4.0 and minimum 2.1) d) gender is an nominal attribute taking values in {male, female}. Assume that the attributes qud and gpa are of major importance and the attribute gender is of a minor importance when assessing ...
effectiveness prediction of memory based classifiers for the
... IB1 is nearest neighbour classifier. It uses normalized Euclidean distance to find the training instance closest to the given test instance, and predicts the same class as this training instance. If several instances have the smallest distance to the test instance, the first one obtained is used. Ne ...
... IB1 is nearest neighbour classifier. It uses normalized Euclidean distance to find the training instance closest to the given test instance, and predicts the same class as this training instance. If several instances have the smallest distance to the test instance, the first one obtained is used. Ne ...
File - Social Sciences @ Groby
... Two groups of patients took part in a trial to compare the effectiveness of two different drug therapies. One of the groups was given Drug A and the other group was given Drug B. All patients completed a rating scale at the start of a ten-week course of treatment and again at the end of the course. ...
... Two groups of patients took part in a trial to compare the effectiveness of two different drug therapies. One of the groups was given Drug A and the other group was given Drug B. All patients completed a rating scale at the start of a ten-week course of treatment and again at the end of the course. ...
Comparative Analysis of Classification Techniques in Data Mining
... In the above expression the “IF”-part of a rule is known as the rule antecedent or precondition. And the “THEN”part is the rule consequent. In the rule antecedent, the condition consists of one or more attribute tests that are logically ANDed. The rule’s consequent contains a class prediction. If th ...
... In the above expression the “IF”-part of a rule is known as the rule antecedent or precondition. And the “THEN”part is the rule consequent. In the rule antecedent, the condition consists of one or more attribute tests that are logically ANDed. The rule’s consequent contains a class prediction. If th ...
Time-Series Classification based on Individualised Error
... Figure 1 depicts a set of labeled instances from two classes that are denoted by triangles and circles. The density in the class of triangles (upper region) is larger than in the class of circles (lower region). We consider two test instances, denoted as ‘1’ and ‘2’, that have to be classified. We a ...
... Figure 1 depicts a set of labeled instances from two classes that are denoted by triangles and circles. The density in the class of triangles (upper region) is larger than in the class of circles (lower region). We consider two test instances, denoted as ‘1’ and ‘2’, that have to be classified. We a ...
Object-Oriented Programming (Java), Unit 2
... are cases where this technique does not give good results • This depends on the distribution of the classifications in the overall data set • Sampling with replacement in some cases can lead to the component error rates being particularly skewed in such a way that they don’t compensate for each othe ...
... are cases where this technique does not give good results • This depends on the distribution of the classifications in the overall data set • Sampling with replacement in some cases can lead to the component error rates being particularly skewed in such a way that they don’t compensate for each othe ...
IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE)
... disease in the early stages [3]. Having so many factors to analyse to diagnose PD, specialist normally makes decisions by evaluating the current test results of their patients. Moreover, the previous decisions made on other patients with a similar condition are also done by them. These are complex p ...
... disease in the early stages [3]. Having so many factors to analyse to diagnose PD, specialist normally makes decisions by evaluating the current test results of their patients. Moreover, the previous decisions made on other patients with a similar condition are also done by them. These are complex p ...
Classifying Text: Classification of New Documents (2
... Remove all MBRs from the partitionList which have a larger distance to the query point q than the current nearest neighbor, NN, of q PartitionList is ascendingly sorted by MinDist to q and ...
... Remove all MBRs from the partitionList which have a larger distance to the query point q than the current nearest neighbor, NN, of q PartitionList is ascendingly sorted by MinDist to q and ...
IMPROVING CLASSIFICATION PERFORMANCE OF K
... by majority vote or by a similarity degree sum of the specified number (k) of nearest points. In majority voting, the number of points in the neighbourhood belonging to each class is counted, and the class to which the highest proportion of points belongs is the most likely classification of x. The ...
... by majority vote or by a similarity degree sum of the specified number (k) of nearest points. In majority voting, the number of points in the neighbourhood belonging to each class is counted, and the class to which the highest proportion of points belongs is the most likely classification of x. The ...
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.