A Multi-Tier Knowledge Discovery Info-Structure Using Ensemble Techniques

Fokus utama kami ialah untuk mempelajari keujudan peraturan-peraturan yang ditemui daripada data-data tanpa catatan serta menjana keputusan yang lebih tepat dan muktamad. Our terminal focus is to learn rules instances that have been discovered from unannotated data and generate results with high accuracy

A Concise Fuzzy Rule Base to Reason Student Performance Based on Rough-Fuzzy Approach

A fuzzy inference system employing fuzzy if then rules able to model the qualitative aspects of human expertise and reasoning processes without employing precise quantitative analyses. This is due to the fact that the problem in acquiring knowledge from human experts is that much of the information is uncertain, inconsistent, vague and incomplete (Khoo and Zhai, 2001; Tsaganou et al., 2002; San Pedro and Burstein, 2003; Yang et al., 2005). The drawbacks of FIS are that a lot of trial and error effort need to be taken into account in order to define the best fitted membership functions (Taylan and Karagözoglu, 2009) and no standard methods exist for transforming human knowledge or experience into the rule base (Jang, 1993)

Rule based Model for Credit Evaluation using Rough Set Approach

Credit plays an important role in the economy. Credit Evaluation of any potential credit application has remained a challenge for banks all over the world till today. Credit evaluation can be defined as a technique that helps lenders to decide whether to grant credit to consumers or not. Its increasing importance can be seen from the growing popularity and application of credit scoring. It is mandatory not only to construct effective credit scoring models to help improve the bottom-line of credit providers, but also to design rule based system for effective credit evaluation system. This paper approaches the use of rough set technique to generate rule based system for credit scoring model

Attribute Reduction for Credit Evaluation using Rough Set Approach

Generation of an Integrated Model is an important technique in the research area. It is a powerful technique to improve the accuracy of classifiers. This approach has been applied to different types of real time data. The unprocessed data leads to give wrong results by using some of the machine learning techniques. For generation of an integrated model attribute reduction and re-sampling technique is necessary. For attribute reduction Rough set is the best approach as it requires less execution time, high Interpretability, high reduction rate and high accurac

A Multi-Tier Knowledge Discovery Info-Structure Using Ensemble Techniques [QA76.9.D35 S158 2007 f rb].

Fokus utama kami ialah untuk mempelajari keujudan peraturan-peraturan yang ditemui daripada data-data tanpa catatan serta menjana keputusan yang lebih tepat dan muktamad. Ini dilakukan melalui kaedah penghibridan yang merangkumi kedua-dua mekanisma berselia dan tidak berselia. Our terminal focus is to learn rules instances that have been discovered from unannotated data and generate results with high accuracy. This is done via a hybridized methodology which features both supervised and unsupervised techniques. Unannotated data without prior classification information could now be useful as our research has brought new insight to knowledge discovery and learning altogether