Scaling Up the Accuracy of Naive-Bayes Classi ers: a Decision
... only a single pass through the data if all attributes are discrete. Naive-Bayes classi ers are also very simple and easy to understand. Kononenko (1993) wrote that physicians found the induced classi ers easy to understand when the log probabilities were presented as evidence that adds up in favor o ...
... only a single pass through the data if all attributes are discrete. Naive-Bayes classi ers are also very simple and easy to understand. Kononenko (1993) wrote that physicians found the induced classi ers easy to understand when the log probabilities were presented as evidence that adds up in favor o ...
PDF - Bentham Open
... a decision tree, select 10% of the data as a test set, and repeat the test 10 times for each data set, taking the average value as the test accuracy rate. From the analysis of Table 1 and Table 2 of the experimental data, the conclusion can be drawn that the proposed algorithm's classification accur ...
... a decision tree, select 10% of the data as a test set, and repeat the test 10 times for each data set, taking the average value as the test accuracy rate. From the analysis of Table 1 and Table 2 of the experimental data, the conclusion can be drawn that the proposed algorithm's classification accur ...
Motion-Alert: Automatic Anomaly Detection in Massive Moving Objects
... 2. To discover anomalies in object movements, motif-based generalization is performed that clusters similar object movement fragments and generalizes the movements based on the associated motifs. 3. With motif-based generalization, objects are put into a multi-level feature space and are classified ...
... 2. To discover anomalies in object movements, motif-based generalization is performed that clusters similar object movement fragments and generalizes the movements based on the associated motifs. 3. With motif-based generalization, objects are put into a multi-level feature space and are classified ...
data classification using support vector machine
... Classification is one of the most important tasks for different application such as text categorization, tone recognition, image classification, micro-array gene expression, proteins structure predictions, data Classification etc. Most of the existing supervised classification methods are based on t ...
... Classification is one of the most important tasks for different application such as text categorization, tone recognition, image classification, micro-array gene expression, proteins structure predictions, data Classification etc. Most of the existing supervised classification methods are based on t ...
Survey on Classification Techniques Used in Data Mining and their
... called the rule antecedent and the else part is the rule consequent. If the condition in the rule antecedent part is true it means that the rule covers the tuple and if it is false then the rule does not cover the tuple. A rule is assessed by it coverage and accuracy. Rules coverage is the percentag ...
... called the rule antecedent and the else part is the rule consequent. If the condition in the rule antecedent part is true it means that the rule covers the tuple and if it is false then the rule does not cover the tuple. A rule is assessed by it coverage and accuracy. Rules coverage is the percentag ...
Improving Clustering Performance on High Dimensional Data using
... collections. The researchers exposed some songs which were similar to many other songs, i.e., frequent neighbors. In high dimensional data, it is difficult to estimate the separation of low density regions and high density regions due to data being very space [3],[5]. It is necessary to chose the pr ...
... collections. The researchers exposed some songs which were similar to many other songs, i.e., frequent neighbors. In high dimensional data, it is difficult to estimate the separation of low density regions and high density regions due to data being very space [3],[5]. It is necessary to chose the pr ...
A Comparative Study of MRI Data using Various Machine Learning
... K-Means clustering algorithm is a well-known unsupervised clustering technique to classify any given input dataset. This algorithm classifies a given dataset into discrete k-clusters using which k-centroids are defined, one for each cluster. The next step is to take each ...
... K-Means clustering algorithm is a well-known unsupervised clustering technique to classify any given input dataset. This algorithm classifies a given dataset into discrete k-clusters using which k-centroids are defined, one for each cluster. The next step is to take each ...
The experiment database for machine learning
... setup, resulting in truly reproducible research. Reference All experiments, including algorithms and datasets, are automatically organized in one resource, creating an overview of the state-of-the-art, and a useful ‘map’ of all known approaches, their properties, and their performance. This also inc ...
... setup, resulting in truly reproducible research. Reference All experiments, including algorithms and datasets, are automatically organized in one resource, creating an overview of the state-of-the-art, and a useful ‘map’ of all known approaches, their properties, and their performance. This also inc ...
Overview Support Vector Machines Lines in R2 Lines in R2 w
... • Notice that it relies on an inner product between the test point x and the support vectors xi • (Solving the optimization problem also involves computing the inner products xi · xj between all pairs of training points) C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Dat ...
... • Notice that it relies on an inner product between the test point x and the support vectors xi • (Solving the optimization problem also involves computing the inner products xi · xj between all pairs of training points) C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Dat ...
Searching for Centers: An Efficient Approach to the Clustering of
... algorithm by comparing with the result of using k-means clustering. For storage requirements of P-trees we refer to [14]. We used synthetic data with 10% noise. The data was generated with no assumptions on continuity in the structural dimension (e.g., location for spatial data, time for multimedia ...
... algorithm by comparing with the result of using k-means clustering. For storage requirements of P-trees we refer to [14]. We used synthetic data with 10% noise. The data was generated with no assumptions on continuity in the structural dimension (e.g., location for spatial data, time for multimedia ...
Full Text - International Journal of Computer Science and Network
... algorithm and hybrid AMPSO algorithm is applied on different benchmark datasets and find out that AMPSO hybrid algorithm is always found a better result than the standard PSO. It was also able to improve the results of the k-Nearest Neighbor algorithm [7]. ...
... algorithm and hybrid AMPSO algorithm is applied on different benchmark datasets and find out that AMPSO hybrid algorithm is always found a better result than the standard PSO. It was also able to improve the results of the k-Nearest Neighbor algorithm [7]. ...
An Evaluation of Gene Selection Methods for Multi
... The performance of the nine ranking methods are shown in Tables 2-5. In the tables, the first row represents the number of selected features. Each cell contains the leave-one-out classification accuracy of SVM, achieved using the corresponding number of top genes ranked by one of the feature selecti ...
... The performance of the nine ranking methods are shown in Tables 2-5. In the tables, the first row represents the number of selected features. Each cell contains the leave-one-out classification accuracy of SVM, achieved using the corresponding number of top genes ranked by one of the feature selecti ...
classification algorithms for big data analysis, a
... The SVM classification algorithm was used to evaluate the tool. WEKA uses the Jhon Platts sequential minimal optimization algorithm for training the SVM (Platt, 1998). In the experiences, a multi-class pairwise (one versus one) SVM classification with a polynomial function kernel was performed, with ...
... The SVM classification algorithm was used to evaluate the tool. WEKA uses the Jhon Platts sequential minimal optimization algorithm for training the SVM (Platt, 1998). In the experiences, a multi-class pairwise (one versus one) SVM classification with a polynomial function kernel was performed, with ...
An Efficient Outlier Detection Using Amalgamation of Clustering and
... estimates of unknown distribution parameters [14, 15] and here lies their limitation. In the definition of depth-based, data objects are organized in convex hull layers in the data space according to peeling depth, and outliers are expected with shallow depth values. As the dimensionality increases, ...
... estimates of unknown distribution parameters [14, 15] and here lies their limitation. In the definition of depth-based, data objects are organized in convex hull layers in the data space according to peeling depth, and outliers are expected with shallow depth values. As the dimensionality increases, ...
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.