comparative analysis of data mining techniques for medical data
... Suguna & Thanushkodi (2010) attempted to probe an improved k-NN using genetic algorithm was utilized to reduce high calculation complexity with low dependency on the training set and no weight difference between each class. Thus, recent studies try to overcome limitation of traditional k-NN, and are ...
... Suguna & Thanushkodi (2010) attempted to probe an improved k-NN using genetic algorithm was utilized to reduce high calculation complexity with low dependency on the training set and no weight difference between each class. Thus, recent studies try to overcome limitation of traditional k-NN, and are ...
Data Visualization and Evaluation for Industry 4.0 using an
... or decrease the attraction between two single objects and can be observed in a 2D space. This can come to use when performing tasks with industry data. An example of this is to detect classes of support requests in the help desk and to route them to the right administrator. The problem with applying ...
... or decrease the attraction between two single objects and can be observed in a 2D space. This can come to use when performing tasks with industry data. An example of this is to detect classes of support requests in the help desk and to route them to the right administrator. The problem with applying ...
Learning Optimization for Decision Tree Classification of Non
... The process of constructing a decision tree with ID3 [11] can be briefly described as follows. For each attribute xj we introduce a set of thresholds {tj,1 , . . . , tj,M } that are equally spaced in the interval [min xj , max xj ]. With each threshold tj,k we will associate two subsets S + (tj,k ) ...
... The process of constructing a decision tree with ID3 [11] can be briefly described as follows. For each attribute xj we introduce a set of thresholds {tj,1 , . . . , tj,M } that are equally spaced in the interval [min xj , max xj ]. With each threshold tj,k we will associate two subsets S + (tj,k ) ...
Data Mining Technique to Predict the Accuracy of the Soil Fertility
... technique. In spite the fact that the least median squares regression is known to produce better results than the classical linear regression technique, from the given set of attributes, the most accurately predicted attribute was “P” (Phosphorous content of the soil) and which was determined using ...
... technique. In spite the fact that the least median squares regression is known to produce better results than the classical linear regression technique, from the given set of attributes, the most accurately predicted attribute was “P” (Phosphorous content of the soil) and which was determined using ...
Paper Title (use style: paper title)
... expectation-maximization algorithm, matrix factorization, principal component analysis and many more. Unsupervised learning can learn models that are having deep hierarchies. It sometimes can be used to cluster the data into categories on the basis of their statistical properties only. Unsupervised ...
... expectation-maximization algorithm, matrix factorization, principal component analysis and many more. Unsupervised learning can learn models that are having deep hierarchies. It sometimes can be used to cluster the data into categories on the basis of their statistical properties only. Unsupervised ...
Paper Title (use style: paper title)
... are some of the major tasks being performed on such data sets. With the increasing amount of data generated by social sharing platforms & apps, the process of data reduction has become inevitable. It involves compressing the data being generated & storing it in a data storage environment. In compute ...
... are some of the major tasks being performed on such data sets. With the increasing amount of data generated by social sharing platforms & apps, the process of data reduction has become inevitable. It involves compressing the data being generated & storing it in a data storage environment. In compute ...
Introduction to R
... d[d<20]: extract all elements of d that are smaller than 20 • d[“age”]: extract column “age” from object d Introduction to R ...
... d[d<20]: extract all elements of d that are smaller than 20 • d[“age”]: extract column “age” from object d Introduction to R ...
Training RBF neural networks on unbalanced data
... the overlaps between different classes and the overlaps between clusters of the same class. The overlaps between different classes have been considered in RBF training algorithms. For example, overlappedreceptive fields of different clusters can improve the performance of the RBF classifier when dea ...
... the overlaps between different classes and the overlaps between clusters of the same class. The overlaps between different classes have been considered in RBF training algorithms. For example, overlappedreceptive fields of different clusters can improve the performance of the RBF classifier when dea ...
Large-Margin Convex Polytope Machine
... could deteriorate the accuracy. The proposed algorithms achieve similar classification accuracy to several nonlinear classifiers, including KNN, decision tree and kernel SVM. However, the training time of the algorithms is often much longer than those nonlinear classifiers (e.g., an order of magnitu ...
... could deteriorate the accuracy. The proposed algorithms achieve similar classification accuracy to several nonlinear classifiers, including KNN, decision tree and kernel SVM. However, the training time of the algorithms is often much longer than those nonlinear classifiers (e.g., an order of magnitu ...
Sharing RapidMiner Workflows and Experiments with OpenML
... approaches do not leverage historical information on the performance of hyperparameter settings on previously seen problems. One simple way to do this is to use meta-learning to build a model that recommends parameter settings [30], or to view multiple algorithm configurations as individual algorith ...
... approaches do not leverage historical information on the performance of hyperparameter settings on previously seen problems. One simple way to do this is to use meta-learning to build a model that recommends parameter settings [30], or to view multiple algorithm configurations as individual algorith ...
... show that the presented algorithm is capable of reducing the number of data vectors as well as the training time of SVMs, while maintaining good accuracy in terms of objective evaluation. The subjective evaluation result of the proposed voice conversion system is compared with the state of the art m ...
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.