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... sequence information (De Smet et al., 2002; Wang & Yang, 2005). In this application, each gene is represented by a histogram vector of motifs, where the number of unique motifs derived from the data can be sometimes larger than the number of genes to be clustered and usually only a small number of m ...
... sequence information (De Smet et al., 2002; Wang & Yang, 2005). In this application, each gene is represented by a histogram vector of motifs, where the number of unique motifs derived from the data can be sometimes larger than the number of genes to be clustered and usually only a small number of m ...
CMAR: Accurate and Efficient Classification Based on Multiple Class
... First, instead of relying on a single rule for classification, CMAR determines the class label by a set of rules. Given a new case for prediction, CMAR selects a small set of high confidence, highly related rules and analyzes the correlation among those rules. To avoid bias, we develop a new techniq ...
... First, instead of relying on a single rule for classification, CMAR determines the class label by a set of rules. Given a new case for prediction, CMAR selects a small set of high confidence, highly related rules and analyzes the correlation among those rules. To avoid bias, we develop a new techniq ...
Inducing Decision Trees with an Ant Colony Optimization Algorithm
... 2.1. Top-down Induction of Decision Trees Decision trees provide a comprehensible graphical representation of a classification model, where the internal nodes correspond to attribute tests (decision nodes) and leaf nodes correspond to the predicted class labels—illustrated in Fig. 1. In order to cla ...
... 2.1. Top-down Induction of Decision Trees Decision trees provide a comprehensible graphical representation of a classification model, where the internal nodes correspond to attribute tests (decision nodes) and leaf nodes correspond to the predicted class labels—illustrated in Fig. 1. In order to cla ...
Classification by Decisión Tree Induction
... A can be reduced by the proportion of samples with unknown valúes oí A. In way, "fractions" of a sample having a missing valué can be partitioned into than one branch at a test node. Other methods may look for the most prob valué of A, or make use of known relationships between A and other attribute ...
... A can be reduced by the proportion of samples with unknown valúes oí A. In way, "fractions" of a sample having a missing valué can be partitioned into than one branch at a test node. Other methods may look for the most prob valué of A, or make use of known relationships between A and other attribute ...
Inducing Decision Trees with an Ant Colony Optimization Algorithm
... 2.1. Top-down Induction of Decision Trees Decision trees provide a comprehensible graphical representation of a classification model, where the internal nodes correspond to attribute tests (decision nodes) and leaf nodes correspond to the predicted class labels—illustrated in Fig. 1. In order to cla ...
... 2.1. Top-down Induction of Decision Trees Decision trees provide a comprehensible graphical representation of a classification model, where the internal nodes correspond to attribute tests (decision nodes) and leaf nodes correspond to the predicted class labels—illustrated in Fig. 1. In order to cla ...
Software Defect Classification using Bayesian Classification
... in the software dataset through two of the classification models namely Bayes net and Naïve Bayes classification. We studied the performance of two classification algorithms on seven publicly available datasets from the NASA MDP Repository. This paper emphasizes on the performance of classification ...
... in the software dataset through two of the classification models namely Bayes net and Naïve Bayes classification. We studied the performance of two classification algorithms on seven publicly available datasets from the NASA MDP Repository. This paper emphasizes on the performance of classification ...
On Multi-Class Cost-Sensitive Learning
... class to another class, while only in some special tasks it is easy to get the cost for every training example. It is noteworthy that the outputs of the research on class-dependent cost-sensitive learning have been deemed as good solutions to learning from imbalanced data sets (Chawla et al. 2002; W ...
... class to another class, while only in some special tasks it is easy to get the cost for every training example. It is noteworthy that the outputs of the research on class-dependent cost-sensitive learning have been deemed as good solutions to learning from imbalanced data sets (Chawla et al. 2002; W ...
ppt
... Single-Link clustering: • corresponds to construction of maximum (minimum) spanning tree for undirected, weighted graph G = (V,E) with V=D, E=DD and edge weight sim(d,d‘) (dist(d,d‘)) for (d,d‘)E • from the maximum spanning tree the cluster hierarchy can be derived by recursively removing the shor ...
... Single-Link clustering: • corresponds to construction of maximum (minimum) spanning tree for undirected, weighted graph G = (V,E) with V=D, E=DD and edge weight sim(d,d‘) (dist(d,d‘)) for (d,d‘)E • from the maximum spanning tree the cluster hierarchy can be derived by recursively removing the shor ...
ADWICE - Anomaly Detection with Real
... is that the relative amount of attacks in the training data is very small compared to normal data, a reasonable assumption that may or may not hold in the real world context for which it is applied. If this assumption holds, anomalies and attacks may be detected based on cluster sizes. Large cluster ...
... is that the relative amount of attacks in the training data is very small compared to normal data, a reasonable assumption that may or may not hold in the real world context for which it is applied. If this assumption holds, anomalies and attacks may be detected based on cluster sizes. Large cluster ...
selection of optimal mining algorithm for outlier detection
... brain and capable of predicting new observations (on specific variables) from other observations (on the same or other variables) after executing a process of so-called learning from existing data[9]. Neural networks essentially comprise three pieces: the architecture or model; the learning algorith ...
... brain and capable of predicting new observations (on specific variables) from other observations (on the same or other variables) after executing a process of so-called learning from existing data[9]. Neural networks essentially comprise three pieces: the architecture or model; the learning algorith ...
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.