Current Issue : July - September Volume : 2018 Issue Number : 3 Articles : 5 Articles
One way to make the knowledge stored in an artificial neural network more intelligible is to extract symbolic rules. However,\nproducing rules from Multilayer Perceptrons (MLPs) is an NP-hard problem. Many techniques have been introduced to generate\nrules from single neural networks, but very few were proposed for ensembles. Moreover, experiments were rarely assessed by\n10-fold cross-validation trials. In this work, based on the Discretized Interpretable Multilayer Perceptron (DIMLP), experiments\nwere performed on 10 repetitions of stratified 10-fold cross-validation trials over 25 binary classification problems. The DIMLP\narchitecture allowed us to produce rules from DIMLP ensembles, boosted shallow trees (BSTs), and Support Vector Machines\n(SVM). The complexity of rulesets was measured with the average number of generated rules and average number of antecedents\nper rule. Fromthe 25 used classification problems, themost complex rulesetswere generated fromBSTs trained by ââ?¬Å?gentle boostingââ?¬Â\nand ââ?¬Å?real boosting.ââ?¬Â Moreover, we clearly observed that the less complex the rules were, the better their fidelity was. In fact, rules\ngenerated from decision stumps trained by modest boosting were, for almost all the 25 datasets, the simplest with the highest\nfidelity. Finally, in terms of average predictive accuracy and average ruleset complexity, the comparison of some of our results to\nthose reported in the literature proved to be competitive....
Due to the adoption of global parameters,DBSCANfails to identify clusters with different and varied densities.To solve the problem,\nthis paper extends DBSCANby exploiting a new density definition and proposes a novel algorithmcalled ...
Graph pattern matching is widely used in big data applications. However, real-world graphs are usually huge and dynamic. A small\nchange in the data graph or pattern graph could cause serious computing cost. Incremental graph matching algorithms can avoid\nrecomputing on the whole graph and reduce the computing cost when the data graph or the pattern graph is updated. The existing\nincremental algorithm PGC IncGPM can effectively reduce matching time when no more than half edges of the pattern graph are\nupdated. However, as the number of changed edges increases, the improvement of PGC IncGPM gradually decreases. To solve this\nproblem, an improved algorithm iDeltaP IncGPM is developed in this paper. For multiple insertions (resp., deletions) on pattern\ngraphs, iDeltaP IncGPM determines the nodes� matching state detection sequence and processes them together. Experimental\nresults show that iDeltaP IncGPM has higher efficiency and wider application range than PGC IncGPM....
Sentiment analysis in a movie review is the needs of today lifestyle. Unfortunately, enormous features make the sentiment of\nanalysis slow and less sensitive. Finding the optimum feature selection and classification is still a challenge. In order to handle an\nenormous number of features and provide better sentiment classification, an information-based feature selection and classification\nare proposed.Theproposed method reducesmore than 90%unnecessary featureswhile the proposed classification scheme achieves\n96% accuracy of sentiment classification. From the experimental results, it can be concluded that the combination of proposed\nfeature selection and classification achieves the best performance so far....
The popularity of social networks has brought the rapid growth of social images which have become an increasingly important\nimage type. One of the most obvious attributes of social images is the tag. However, the sate-of-the-art methods fail to fully\nexploit the tag information for saliency detection.Thus this paper focuses on salient region detection of social images using both\nimage appearance features and image tag cues. First, a deep convolution neural network is built, which considers both appearance\nfeatures and tag features. Second, tag neighbor and appearance neighbor based saliency aggregation terms are added to the saliency\nmodel to enhance salient regions.The aggregation method is dependent on individual images and considers the performance gaps\nappropriately. Finally, we also have constructed a new large dataset of challenging social images and pixel-wise saliency annotations\nto promote further researches and evaluations of visual saliency models. Extensive experiments show that the proposed method\nperforms well on not only the new dataset but also several state-of-the-art saliency datasets....
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