“Bag of words” model, acronym extraction, authorship ascription, coordinate matching, data mining, document clustering, document frequency, document retrieval, document similarity metrics, entity extraction, hidden Markov models, hubs and authorities, information extraction, information retrieval, key-phrase assignment, key-phrase extraction, knowledge engineering, language identification, link...

There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...

The problem of information extraction (IE) regards automatic generation of extraction programs (also called wrappers). Similar to compiler generator, the core problem is to generate extraction rules. In this paper, we introduce IEPAD (an acronym for Information Extraction based on PAttern Discovery), a system that generalizes extraction patterns from Web pages without user-labeled examples. The...

Associative classification rule mining (ACRM) methods operate by association rule mining (ARM) to obtain classification rules from a previously classified data. In ACRM, classifiers are designed through two phases: rule extraction and rule selection. In this paper, the ACRM problem is treated as a multiobjective problem rather than a single objective one. As the problem is a discrete combinator...

This paper presents a new approach to rule extraction from Support Vector Machines (SVMs). SVMs have been applied successfully in many areas with excellent generalization results; rule extraction can offer explanation capability to SVMs. We propose to approximate the SVM classification boundary through querying followed by clustering, searching and then to extract rules by solving an optimizati...

The utility of data-driven techniques in the end-to-end problem of temporal information extraction is unclear. Recognition of temporal expressions yields readily to machine learning, but normalization seems to call for a rule-based approach. We explore two aspects of the (potential) utility of data-driven methods in the temporal information extraction task. First, we look at whether improving r...

This paper demonstrates a mechanism whereby rules can be extracted from a feedforward neural network trained to characterize the money­ price relationship, defined as the relationship between the rate of growth of the money supply and inflation. Monthly Divisia component data is encoded and used to train a group of candidate connectionist architectures. One candidate is selected for rule extrac...

This paper presents a new approach to rule extraction from Support Vector Machines. SVMs have been applied successfully in many areas with excellent generalization results; rule extraction can offer explanation capability to SVMs. We propose to approximate the SVM classification boundary through querying followed by clustering, searching and then to extract rules by solving an optimization prob...