Developing robust and less complex models capable of coping with environment volatility is the quest of every data mining project. This study attempts to establish heuristics for investigating the impact of noise in instance attributes data on learning model volatility. In addition, an alternative method for determining attribute importance and feature ranking, based on attribute sensitivity to...

This paper studies the eeects on decision tree learning of constructing four types of attribute (conjunc-tive, disjunctive, M-of-N, and X-of-N representations). To reduce eeects of other factors such as tree learning methods, new attribute search strategies, evaluation functions, and stopping criteria, a single tree learning algorithm is developed. With diierent option settings, it can construc...

The effects of social influence and network autocorrelation suggest that both network structure and node attribute information should inform the tasks of link prediction and node attribute inference. However, the algorithmic question of how to efficiently incorporate these two sources of information remains largely unanswered. We propose a Social-Attribute Network (SAN) model that gracefully in...

Eliminating irrelevant attributes prior to induction boosts the performance of many learning algorithms. Relevance, however, is no guarantee of usefulness to a particular learner. We test two methods of finding relevant attributes, FOCUS and RELIEF, to see how the attributes they select perform with ID3/C4.5 on two learning problems from a calendar scheduling domain. A more direct attribute sel...

auctions have been as a competitive method of buying and selling valuable or rare items for a long time. single-sided auctions in which participants negotiate on a single attribute (e.g. price) are very popular. double auctions and negotiation on multiple attributes create more advantages compared to single-sided and single-attribute auctions. nonetheless, this adds the complexity of the auctio...

From the customer perspective, Business-to-Customer (B2C) is to be defined as a channel. In order to survive, grow, and succeed, IS managers should design B2C channels with attributes that maximize customer satisfaction. The authors investigate B2C channel attributes as antecedents for customer satisfaction. The relationship between the attribute-level performance and overall satisfaction is al...

Problem statement: Predicting the value for missing attributes is an important data preprocessing problem in data mining and knowledge discovery tasks. Several methods have been proposed to treat missing data and the one used more frequently is deleting instances containing at least one missing value of a feature. When the dataset has minimum number of missing attribute values then we can negle...