Camelia Lemnaru

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Spamming has become a time consuming and expensive problem for which several new directions have been investigated lately. This paper presents a new approach for a spam detection filter. The solution developed is an offline application that uses the k-Nearest Neighbor (kNN) algorithm and a pre-classified email data set for the learning process.
In the field of data mining, one of the main objectives is to achieve the highest possible classification accuracy. This paper presents a classifier fusion system based on the principles of the Dempster-Shafer theory of evidence combination. It allows one to combine evidence from different sources and arrive at a degree of belief (represented by a belief(More)
This paper focuses on solutions to two NP-Complete problems: k-SAT and the knapsack problem. We propose a new parallel genetic algorithm strategy on the CUDA architecture, and perform experiments to compare it with the sequential versions. We show how these problems can benefit from the GPU solutions, leading to significant improvements in speedup while(More)
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(More)
Current important challenges in data mining research are triggered by the need to address various particularities of real-world problems, such as imbalanced data and error cost distributions. This paper presents Distributed Evolutionary Cost-Sensitive Balancing, a distributed methodology for dealing with imbalanced data and -- if necessary -- cost(More)