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This paper presents an enhanced method of partitioning a dataset into clusters when dealing with the handwritten signature recognition problem. The goal of the present system is improving the performance of two previously developed systems. In the first version of our system we dealt with data extraction from signature images and obtained a recognition rate of 91.04% using the Naïve Bayes classifier...
Sentiment prediction for text has been an intriguing subject for the last few years. The goal of it is to automatically indicate the positive or negative attitude towards a topic of interest. The proliferation of user generated content on the World Wide Web has made it possible to perform large scale mining of public opinion. This paper presents an original implementation of a system that integrates...
Adaptive e-learning systems are the newest paradigm in modern learning approaches. One of the key factors in such systems is the correct and continuous identification of the user learning style, such as to provide the most appropriate content presentation to each individual user. This paper presents a new possibility for identifying the initial user typology, based on static features, in an adaptive...
In data mining, there is no learning algorithm which attains the highest accuracy on any dataset. Multilevel arbiter and combiner arbiter are presented in this paper, as techniques to integrate classifiers induced from partitioned data, having as optimization criterion the accuracy of a given dataset. Experimental evaluations have shown that an arbiter tree can be found having similar or higher predictive...
Meta-learning is currently a hot research topic in machine learning, which has emerged from the need to support data mining automation in issues related to algorithm and parameter selection. Finding the best learning strategy for a new domain/problem can prove to be an expensive and time-consuming process even for the experienced analysts. This paper presents a new meta-learning system, designed to...
High accuracy is essential to any data mining process. A large part of the factors which influence the success of a data mining problem reside in the quality of the data used. Feature selection represents one of the tools which can refine a dataset before presenting it to a learning scheme. This paper analyzes a wrapper approach for feature selection, with the purpose of boosting the classification...
The concept of Grid Computing addresses the next evolutionary step of distributed computing. Offering storage and computation capabilities to users not having those resources is the ultimate goal of grid computing. In this paper we introduce a taxonomy for grid applications, aiming at a better understanding of the categories of applications that could benefit from gridification. The proposed taxonomy...
The applicability of learning methods to raw data coming from different areas of human activity is one of the main concerns in data mining research today. This paper emphasizes the need for a sound preprocessing method to improve the quality of the learning process through data imputation. Three classification methods we have previously developed are presented, with a focus on their evaluations. The...
Medical diagnosis and prognosis is an emblematic example for classification problems. Machine learning could provide invaluable support for automatically inferring diagnostic rules from descriptions of past cases, making the diagnosis process more objective and reliable. Since the problem involves both test and misclassification costs, we have analyzed ICET, the most prominent approach in the literature...
Artificial neural networks are known to have strong generalization abilities, but they entirely lack comprehensibility, due to their connectionist nature. Neural network ensembles augment this characteristic, making them less appealing in domains where comprehensibility is as important as accuracy. This paper presents the implementation of a new system based on a method for combining ensembles of...
Several classifier combination approaches have been proposed in machine learning literature in order to enhance the performance of simple learning schemes. This paper presents a new classifier fusion system based on the principles of the Dempster-Shafer theory of evidence combination. The system tackles the advantages of combining different sources of information to attain a high degree of stability...
The concept of grid computing addresses the next evolutionary step of distributed computing. The goal of this computing model is to make a better use of distributed resources, put them together in order to achieve higher throughput and be able to tackle large scale computation problems. Performance gain is intended at each and every level of an application. Grid data access is achieved by means of...
In recent years, data mining has started to receive increasing interest as a method of complementing domain specific expertise in various spheres of human activity. Apart from data specific issues, a key particularity of many real world problems, such as medical diagnosis, are the costs involved, the most important being the test and the misclassification costs. This paper evaluates ProICET, a new...
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