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The widely known classifier chains method for multi-label classification, which is based on the binary relevance (BR) method, overcomes the disadvantages of BR and achieves higher predictive performance, but still retains important advantages of BR, most importantly low time complexity. Nevertheless, despite its advantages, it is clear that a randomly arranged chain can be poorly ordered. We overcome...
Generalizing hypotheses based on the past data in order to predict the future is the essential core of human learning. Various successful methods and techniques have been developed so far that perform some sort of classification of current data in order to predict future unseen cases. Multi class classification problems are among them as well. In many domains in spite of these automatic techniques,...
With an ever-increasing amount of information made available via the Internet, it is getting more and more difficult to find the relevant pieces of information. Recommender systems have thus become an essential part of information technology. Although a lot of research has been devoted to this area, the factors influencing the quality of recommendations are not completely understood. This paper examines...
Parallelizability of an algorithm is nowadays a highly desirable property as computer hardware is becoming increasingly parallel. In this paper, a formulation of the particle filtering algorithm, suitable for parallel or distributed computing, is proposed. From the particle set, a series expansion is fitted to the posterior probability density function. The global information provided by the particles...
As a tool to serve the elderly and disabled people, intelligent wheelchair may work in spacious and dynamic environments, such as parking lot. One difficulty of working in such scenarios is spaciousness and large scale which increases the difficulty of mapping. And the other is that there are various dynamic obstacles with different mobile frequency in the environment, which poses a new challenge...
Huge amount of user request data is generated in web-log. Predicting users' future requests based on previously visited pages is important for web page recommendation, reduction of latency, on-line advertising etc. These applications compromise with prediction accuracy and modelling complexity. we propose a Web Navigation Prediction Framework for webpage Recommendation(WNPWR) which creates and generates...
The bank direct marketing campaign for offering products that meet the customers' needs is the challenge problems. The bank direct marketing data analysis is important work that helps the banks predict whether customers will sign a long term deposits with the banks. The method that can predict such customers' needs can be profitable to the banks for improving their marketing campaign strategies. Unfortunately,...
Data Streams are instances that arrive at a very rapid rate with changes in underlying conceptual distributions. Many ensemble learning approaches were developed to handle these changes in the dataset, which proved to be better than a single classifier system. In our work, we will discuss the framework of our new approach, Double Weighted Methodology and empirically prove it to be better than the...
In this research, we propose using time context to improve predictive accuracy and quality of collaborative filtering for music recommendation. We use time contextual information called micro-profiling. Thus, each user has multiple micro profiles, in particular, six-time slots instead of a single profile. The recommendation is performed depended on these micro-profiling. Our method takes into account...
Cost-performance trade off is one of the critical challenges in cloud computing environments. Predictive auto-scaling systems mitigate this issue by scaling in/out system automatically based on performance prediction results. The goal of this research is to investigate the impact of different prediction results on the scaling actions generated by predictive auto-scaling systems. In this study, predictive...
With the rapid development in science and technology, data acquisition, storage and mining technology are widely applied to various fields. All aspects of people's lives are recorded as data. Through the analyzing and arranging of data, people can get a lot of valuable information. In this paper, support vector machine (SVM), least squares support vector machine (LSSVM) and partial least squares (PLS)...
There are many popular algorithms to recognize the human voice. The good algorithm not only results the high recognition accuracy, but also robust to noises. Several experiments are done in this research to verify the performance of the Neuro-fuzzy system to recognize the human voice. Eight words in Thai language recorded in a different environment, syllable and pronunciations are used as a data set...
QoS prediction for Web services is a hot research problem in the field of services computing. As one of the most important methods for QoS prediction, Collaborative Filtering (CF) makes prediction based on the historical QoS data contributed by similar users and services. The key issue in this process is to detect the unreliable data offered by untrustworthy users, which has attracted limited attentions...
Churn prediction, or the task of identifying customers who are likely to discontinue use of a service, is an important and lucrative concern of firms in many different industries. As these firms collect an increasing amount of large-scale, heterogeneous data on the characteristics and behaviors of customers, new methods become possible for predicting churn. In this paper, we present a unified analytic...
QoS prediction has become an important step in service recommending and selecting. Most QoS prediction approaches are using collaborative filtering as a prediction technique. But collaborative filtering may suffer from data sparsity problem which degrade the prediction accuracy. In order to alleviate the data sparsity problem of collaborative filtering, we presented a hybrid QoS prediction approach...
The KNN algorithm has a significant effect on classification prediction in Data Mining. In order to solve the drawbacks for KNN algorithm to reduce the costs of the calculation and increase the accuracy, this paper proposed a prototype generation method with same class label proportion for classification to ensure that each class has at least a prototype to be represented. We compare the average success...
Random Forests have been used as effective ensemble models for classification. We present in this paper a new type of Random Forests (RFs) called Red(uced) RF that adopts a new dynamic data reduction principle and a new voting mechanism called Priority Vote Weighting (PV) which improve accuracy, execution time and AUC values compared to Breiman's RF. Red-RF also shows that the strength of a random...
Nowadays, more and more service consumers pay great attention to QoS (Quality of Service) when they find and select appropriate Web services. For most of the approaches to QoS-aware Web service recommendation, the list of Web services recommended to target users is generally obtained based on rating-oriented predictions, aiming at predicting the potential ratings that a target user may assign to the...
Support vector machine (SVM) and its derivative algorithms have been increasingly used to predict algal blooms recently. However, its computation complexity remains an annoying problem. To improve the time cost of SVM, a hybrid approach is proposed in this paper based on Partial Least Square (PLS) feature extraction and Core Vector Machine Regression (CVR) algorithm. We describe the principle of our...
Existing service recommendation methods, that employ memory-based collaborative filtering (CF) techniques, compute the similarity between users or items using nonfunctional attribute values obtained at service invocation. However, using these nonfunctional attribute values from invoked services alone in similarity computation for personalized service recommendation is not sufficient. This is because...
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