A Novel Memetic Feature Selection Algorithm

Feature selection is a problem of finding efficient features among all features in which the final feature set can improve accuracy and reduce complexity. In feature selection algorithms search strategies are key aspects. Since feature selection is an NP-Hard problem; therefore heuristic algorithms have been studied to solve this problem. In this paper, we have proposed a method based on memetic algorithm to find an efficient feature subset for a classification problem. It incorporates a filter method in the genetic algorithm to improve classification performance and accelerates the search in identifying core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the multivariate feature information. Empirical study on commonly data sets of the university of California, Irvine shows that the proposed method outperforms existing methods

A niching memetic algorithm for simultaneous clustering and feature selection

Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both clustering and feature selection, without making any a priori assumption about the number of clusters. Within the NMA_CFS procedure, a variable composite representation is devised to encode both feature selection and cluster centers with different numbers of clusters. Further, local search operations are introduced to refine feature selection and cluster centers encoded in the chromosomes. Finally, a niching method is integrated to preserve the population diversity and prevent premature convergence. In an experimental evaluation we demonstrate the effectiveness of the proposed approach and compare it with other related approaches, using both synthetic and real data

Towards Prediction of Pancreatic Cancer Using SVM Study Model

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Toward optimal feature selection using ranking methods and classification algorithms

We presented a comparison between several feature ranking methods used on two real datasets. We considered six ranking methods that can be divided into two broad categories: statistical and entropy-based. Four supervised learning algorithms are adopted to build models, namely, IB1, Naive Bayes, C4.5 decision tree and the RBF network. We showed that the selection of ranking methods could be important for classification accuracy. In our experiments, ranking methods with different supervised learning algorithms give quite different results for balanced accuracy. Our cases confirm that, in order to be sure that a subset of features giving the highest accuracy has been selected, the use of many different indices is recommended

Unsupervised Distributed Feature Selection for Multi-view Object Recognition

Object recognition accuracy can be improved when information frommultiple views is integrated, but information in each view can oftenbe highly redundant. We consider the problem of distributed objectrecognition or indexing from multiple cameras, where thecomputational power available at each camera sensor is limited andcommunication between sensors is prohibitively expensive. In thisscenario, it is desirable to avoid sending redundant visual featuresfrom multiple views, but traditional supervised feature selectionapproaches are inapplicable as the class label is unknown at thecamera. In this paper we propose an unsupervised multi-view featureselection algorithm based on a distributed compression approach.With our method, a Gaussian Process model of the joint viewstatistics is used at the receiver to obtain a joint encoding of theviews without directly sharing information across encoders. Wedemonstrate our approach on recognition and indexing tasks withmulti-view image databases and show that our method comparesfavorably to an independent encoding of the features from eachcamera

Clustering Perspective in Attribute based data Set in the Collaborative Learning Behavior

Abstract Technology has its own way to understand the aspect of human social life, in this prospect we consider collective context in the sense of an individual scalability of the Data set in the perspective of considering the network is a network that relies on computing power of its clients rather than in the network itself on attribute. a set of information-theoretic techniques based on clustering that discover duplicate, or almost duplicate, tuples and attribute values in a relation instance. From the information collected about the values, we then presented an approach that groups attribute so that duplication in each group is as high as possible. The groups of attributes with large duplication provide important clues for the re-design of the schema of a relation. We consider the context using these clues, since we consider the node mechanism flow putting forward to the level of highest cluster in the social networking domain

Inferring Intent from Interaction with Visualization

Today\u27s state-of-the-art analysis tools combine the human visual system and domain knowledge, with the machine\u27s computational power. The human performs the reasoning, deduction, hypothesis generation, and judgment. The entire burden of learning from the data usually rests squarely on the human user\u27s shoulders. This model, while successful in simple scenarios, is neither scalable nor generalizable. In this thesis, we propose a system that integrates advancements from artificial intelligence within a visualization system to detect the user\u27s goals. At a high level, we use hidden unobservable states to represent goals/intentions of users. We automatically infer these goals from passive observations of the user\u27s actions (e.g., mouse clicks), thereby allowing accurate predictions of future clicks. We evaluate this technique with a crime map and demonstrate that, depending on the type of task, users\u27 clicks appear in our prediction set 79\% -- 97\% of the time. Further analysis shows that we can achieve high prediction accuracy after only a short period (typically after three clicks). Altogether, we show that passive observations of interaction data can reveal valuable information about users\u27 high-level goals, laying the foundation for next-generation visual analytics systems that can automatically learn users\u27 intentions and support the analysis process proactively