Classification Algorithm in Data Mining Based on Maximum Exponential Class Counts Technique

A new split attribute measure for decision tree node split during decision tree creation is proposed. The new split measure consists of the sum of class counts of distinct values of categorical attributes in the dataset. Larger counts induce larger partitions and smaller trees there by favors to the determination of the best spit attribute. The new split attribute measure is termed as maximum exponential class counts (MECC). Experiment results obtained over several UCI machine learning categorical datasets predominantly indicate that the decision tree models created based on the proposed MECC node split attribute technique provides better classification accuracy results and smaller trees in size than the decision trees created using popular gain ratio, normalized gain ratio and gini-index measures. The experimental results are mainly focused on performing and analyzing the results from the node splitting measures alone.

Hierarchical mask creation for intelligent image coding using saliency maps

In this paper we analyze basic mask creation methods for intelligent image coding using saliency maps. For saliency maps based image coding we use specific extension of SPIHT algorithm called SM SPIHT related to region of interest encoding but extending this approach further, ending with individual weight of importance for each pixel in image using the form of saliency map. This approach is proved to be effective. In this article we analyze impact of different basic hierarchical mask creation methods, which have impact on error separation between salient and not salient parts of the image. The results indicate that proposed mask creation method outperforms JPEG2000 based mask tree creation method.

A novel algorithm for extracting the user reviews from web pages

Extracting the user reviews in websites such as forums, blogs, newspapers, commerce, trips, etc. is crucial for text processing applications (e.g. sentiment analysis, trend detection/monitoring and recommendation systems) which are needed to deal with structured data. Traditional algorithms have three processes consisting of Document Object Model (DOM) tree creation, extraction of features obtained from this tree and machine learning. However, these algorithms increase time complexity of extraction process. This study proposes a novel algorithm that involves two complementary stages. The first stage determines which HTML tags correspond to review layout for a web domain by using the DOM tree as well as its features and decision tree learning. The second stage extracts review layout for web pages in a web domain using the found tags obtained from the first stage. This stage is more time-efficient, being approximately 21 times faster compared to the first stage. Moreover, it achieves a relatively high accuracy of 96.67% in our experiments of review block extraction.

Parameter Optimization Based on Genetic Algorithm in the Research of Equivalent Pruning Effect on Fuzzy Decision Tree

for a parameter α that played an important role in the course of fuzzy decision tree creation ,there exists a specific interval, within which gradual increasing of the parameter α may have the same effect as the crisp decision tree post pruning on the size and test accuracy, and there exists a optimum value of α within this specific interval, When α gets the value, can make the fuzzy decision tree reach its performance optimal. To obtain α optimum value , this paper proposed a method of optimizing parameter based on genetic algorithm and proved the validity of the method through comparing with the relative experiment.