A hybrid system by evolving case-based reasoning with genetic algorithm in wholesaler's returning book forecasting

Chang, Pei‐Chann; Lai, Chien-Yuan; Lai, K. Robert

doi:10.1016/j.dss.2006.02.014

Cited by 51 publications

(19 citation statements)

References 13 publications

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“…Chang and Lai [15] integrated the SOM and CBR for sales forecasts of newly released books. Chang et al [16] evolved a CBR system with genetic algorithm for wholesaler returning book forecasting. Chun and Park [17] devised a regression CBR for financial forecasting, which applies different weights to independent variables before finding similar cases.…”

Section: Introductionmentioning

confidence: 99%

Hepatitis Disease Diagnosis Using Hybrid Case Based Reasoning and Particle Swarm Optimization

Neshat

Sargolzaei

Toosi

et al. 2012

ISRN Artificial Intelligence

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Correct diagnosis of a disease is one of the most important problems in medicine. Hepatitis disease is one of the most dangerous diseases that affect millions of people every year and take man’s life. In this paper, the combination of two methods of PSO and CBR (case-based reasoning) has been used to diagnose hepatitis disease. First, a case-based reasoning method is workable to preprocess the data set therefore a weight vector for every one feature is extracted. A particle swarm optimization model is then practical to assemble a decision-making system based on the selected features and diseases recognized. Many researchers have tried to have a more accurate diagnosis of the disease through the use of various methods. The data used has been taken from the site UCI called hepatitis disease. This database has 155 records and 19 fields. This method was compared with five other classification methods and given the results of the proposed method (CBR-PSO), better results were achieved. The proposed method could diagnose hepatitis disease with the accuracy of 93.25%.

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Section: Introductionmentioning

confidence: 99%

Hepatitis Disease Diagnosis Using Hybrid Case Based Reasoning and Particle Swarm Optimization

Neshat

Sargolzaei

Toosi

et al. 2012

ISRN Artificial Intelligence

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“…The application of GA to case-based reasoning has been studied to the retrieval phase for feature and instance learning, as in [54,55,56]. We understand that in the future we need to complement our approach with these studies and consider the synergies between feature learning and classifier weight learning, together with local similarity measures, such as those studied in [57].…”

Section: Comparison With Similar Toolsmentioning

confidence: 99%

eXiT*CBR.v2: Distributed case-based reasoning tool for medical prognosis

Plà

López

Gay

et al. 2013

Decision Support Systems

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In this work we propose a user-friendly medically oriented tool for prognosis development systems and experimentation under a case-based reasoning methodology. The tool enables health care collaboration practice to be mapped in cases where different doctors share their expertise, for example, or where medical committee composed of specialists from different fields work together to achieve a final prognosis. Each agent with a different piece of knowledge classifies the given cases through metrics designed for this purpose. Since multiple solutions for the same case is useless, agents collaborate among themselves in order to achieve a final decision through a coordinated schema. For this purpose, the tool provides a weighted voting schema and an evolutionary algorithm (genetic algorithm) to learn robust weights. Moreover, to test the experiments, the tool includes stratified cross-validation methods which take the collaborative environment into account. In this paper the different collaborative facilities offered by the tool are described. A sample usage of the tool is also provided.

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“…These result from misleading assumptions about the continuity of driving factors and their interrelations (Barnett 1988). As the volatility of the environments of organizations increases, existing models are unable to cope with this problem (Chang, Lai, and Lai 2006). Dealing with emerging risks requires a combination of established and new models, as well as more efficient, unified, and adaptive applications (Leigh, Purvis, and Ragusa 2002).…”

Section: 3mentioning

confidence: 99%

“…As their environment today is more volatile than ever (Chang, Lai, and Lai 2006;Cinar and Kayakutlu 2010), managing companies has become increasingly complex. In recent times, the 2008/2009 economic crisis provided a strong impulse for companies to focus earlier on emerging threats and opportunities (Makridakis, Hogarth, and Gaba 2010).…”

Section: Introductionmentioning

confidence: 99%

Improving Environmental Scanning Systems Using Bayesian Networks

Welter¹,

Mayer

Quick

2013

Bus Res

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A hybrid system by evolving case-based reasoning with genetic algorithm in wholesaler's returning book forecasting

Cited by 51 publications

References 13 publications

Hepatitis Disease Diagnosis Using Hybrid Case Based Reasoning and Particle Swarm Optimization

Hepatitis Disease Diagnosis Using Hybrid Case Based Reasoning and Particle Swarm Optimization

eXiT*CBR.v2: Distributed case-based reasoning tool for medical prognosis

Improving Environmental Scanning Systems Using Bayesian Networks

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