Evolutionary Optimization of Radial Basis Function Classifiers for
... 2) Evolutionary algorithms (EA, [7], [8]) are used for architecture optimization (combined feature and model selection) of the RBF networks. Here, this class of optimization algorithms is chosen because the search space is high-dimensional and the objective function is noisy, deceptive, multimodal, ...
... 2) Evolutionary algorithms (EA, [7], [8]) are used for architecture optimization (combined feature and model selection) of the RBF networks. Here, this class of optimization algorithms is chosen because the search space is high-dimensional and the objective function is noisy, deceptive, multimodal, ...
Continuous Trend-Based Classification of Streaming Time Series
... suggests the use of the main memory in order to avoid costly I/O operations. The second requirement states that random access to past stream data is not supported. Therefore, any computations that must be performed on the stream should be incremental, in order to avoid reading past stream values. In ...
... suggests the use of the main memory in order to avoid costly I/O operations. The second requirement states that random access to past stream data is not supported. Therefore, any computations that must be performed on the stream should be incremental, in order to avoid reading past stream values. In ...
Finding Frequent Items in Data Streams
... ♦ MAJORITY algorithm solves the problem in arrivals only model ♦ Start with a counter set to zero. For each item: If counter is zero, pick up the item, set counter to 1 – Else, if item is same as item in hand, increment counter ...
... ♦ MAJORITY algorithm solves the problem in arrivals only model ♦ Start with a counter set to zero. For each item: If counter is zero, pick up the item, set counter to 1 – Else, if item is same as item in hand, increment counter ...
Discovering Decision Trees
... how well a given attribute separates the learning examples according to their classification. • Heuristic: prefer the attribute that produces the “purest” sub-nodes and leads to the smallest tree. ...
... how well a given attribute separates the learning examples according to their classification. • Heuristic: prefer the attribute that produces the “purest” sub-nodes and leads to the smallest tree. ...
Mining Massive Data Streams
... at least 1 − δ ∗ , it suffices to use δ = δ ∗ /[da(b − a)] in each comparison. Thus, in each search step, we need to use enough examples n i to make i = f (ni , δ ∗ /[da(b − a)], s) < ri , where ri is the difference in accuracy between the a th and (a + 1)th best classifiers (on ni examples, at the ...
... at least 1 − δ ∗ , it suffices to use δ = δ ∗ /[da(b − a)] in each comparison. Thus, in each search step, we need to use enough examples n i to make i = f (ni , δ ∗ /[da(b − a)], s) < ri , where ri is the difference in accuracy between the a th and (a + 1)th best classifiers (on ni examples, at the ...
Full Report - Aditi Patil
... characterize the native data behaviour. The sub-clusters contain significantly less data points than remaining clusters, are termed as outliers. Cluster analysis [7] is a popular machine learning approach to group similar data instances into clusters. It is either used as a stand-alone tool to get a ...
... characterize the native data behaviour. The sub-clusters contain significantly less data points than remaining clusters, are termed as outliers. Cluster analysis [7] is a popular machine learning approach to group similar data instances into clusters. It is either used as a stand-alone tool to get a ...
Grouping related attributes - RIT Scholar Works
... exist in this space. However, there are serious limitations here. In most practical instances where groups of features are required, m n. In general clustering solutions, the algorithm attempts to seek a clustering of the data points described in their various dimensions or in other words describe ...
... exist in this space. However, there are serious limitations here. In most practical instances where groups of features are required, m n. In general clustering solutions, the algorithm attempts to seek a clustering of the data points described in their various dimensions or in other words describe ...
An Efficient Clustering Algorithm for Outlier Detection in Data Streams
... Given a set of data points and the required number of k clusters and k is specified by the user, the k-means algorithm are partitions the data into k clusters based on a distance function. The k-means method partitions the data into k clusters, where as the k is supplied by the user. The algorithm p ...
... Given a set of data points and the required number of k clusters and k is specified by the user, the k-means algorithm are partitions the data into k clusters based on a distance function. The k-means method partitions the data into k clusters, where as the k is supplied by the user. The algorithm p ...
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ARAA: A Fast Advanced Reverse Apriori Algorithm for Mining
... artificial intelligence methods. It aims at finding interesting correlations, frequent patterns, associations among sets of items [1] in the data sources. Association rule mining has been a topic of research in data mining. In order to find the associations among large set of items in a transaction ...
... artificial intelligence methods. It aims at finding interesting correlations, frequent patterns, associations among sets of items [1] in the data sources. Association rule mining has been a topic of research in data mining. In order to find the associations among large set of items in a transaction ...
Classification and Decision Trees
... Definition Classification is a data mining function that assigns items in a collection to target categories or classes. ...
... Definition Classification is a data mining function that assigns items in a collection to target categories or classes. ...
Mining High-Speed Data Streams - Washington
... served ∆G > then the Hoeffding bound guarantees that the true ∆G ≥ ∆G − > 0 with probability 1 − δ, and therefore that Xa is indeed the best attribute with probability 1 − δ. This is valid as long as the G value for a node can be viewed as an average of G values for the examples at that node, a ...
... served ∆G > then the Hoeffding bound guarantees that the true ∆G ≥ ∆G − > 0 with probability 1 − δ, and therefore that Xa is indeed the best attribute with probability 1 − δ. This is valid as long as the G value for a node can be viewed as an average of G values for the examples at that node, a ...
Mining High-Speed Data Streams
... served ∆G > then the Hoeffding bound guarantees that the true ∆G ≥ ∆G − > 0 with probability 1 − δ, and therefore that Xa is indeed the best attribute with probability 1 − δ. This is valid as long as the G value for a node can be viewed as an average of G values for the examples at that node, a ...
... served ∆G > then the Hoeffding bound guarantees that the true ∆G ≥ ∆G − > 0 with probability 1 − δ, and therefore that Xa is indeed the best attribute with probability 1 − δ. This is valid as long as the G value for a node can be viewed as an average of G values for the examples at that node, a ...
preprocessing - Soft Computing Lab.
... data analysis algorithms only accept categorical attributes • Some techniques ...
... data analysis algorithms only accept categorical attributes • Some techniques ...
Evolutionary computing for knowledge discovery in medical diagnosis
... is different from the problem of data classification addressed in this paper. Unlike GA-P, a two-phase evolutionary process is adopted in our approach, i.e. the hybrid evolutionary algorithm is applied to generate good rules in the first phase, which are then used to evolve comprehensible rule sets ...
... is different from the problem of data classification addressed in this paper. Unlike GA-P, a two-phase evolutionary process is adopted in our approach, i.e. the hybrid evolutionary algorithm is applied to generate good rules in the first phase, which are then used to evolve comprehensible rule sets ...
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.