Dynamic adaptive ensemble case-based reasoning: application to stock market prediction
Expert Systems with ApplicationsPublished 7 January 2005
Se‐Hak Chun, Yoon‐Joo Park
Citations74
SJR quartileQ1
SJR score1.85
SNIP2.55
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A new learning technique which extracts new case vectors using Dynamic Adaptive Ensemble CBR (DAE CBR), which originates from finding combinations of parameter and updating and applying an optimal CBR model to application or domain area is proposed.
Abstract
This paper proposes a new learning technique which extracts new case vectors using Dynamic Adaptive Ensemble CBR (DAE CBR). The main idea of DAE CBR originates from finding combinations of parameter and updating and applying an optimal CBR model to application or domain area. These concepts are investigated against the backdrop of a practical application involving the prediction of a stock market index.
Keywords
Computer ScienceDecision Sciences
Machine LearningBagging predictors
16,377 Citations1996Leo Breiman
Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
Springer series in information sciencesSelf-Organization and Associative Memory
8,770 Citations1989Teuvo Kohonen
IEEE Transactions on Pattern Analysis and Machine IntelligenceNeural network ensembles
4,269 Citations1990Lars Kai Hansen, Peter Salamon
It is shown that the remaining residual generalization error can be reduced by invoking ensembles of similar networks, which helps improve the performance and training of neural networks for classification.
Machine LearningThe Strength of Weak Learnability
3,293 Citations1990Robert E. Schapire
In this paper, a method is described for converting a weak learning algorithm into one that achieves arbitrarily high accuracy, and it is shown that these two notions of learnability are equivalent.
Machine LearningThe strength of weak learnability
2,434 Citations1990Robert E. Schapire
In this paper, it is shown that the two notions of learnability are equivalent, and a method is described for converting a weak learning algorithm into one that achieves arbitrarily high accuracy.
Springer eBooksSelf-organization and associative memory: 3rd edition
1,230 Citations1989T. Kohonen
This work prospectively examined the association between magnesium intake and incidence of Type 2 diabetes in a general Japanese population.
Artificial Intelligence in MedicineLung cancer cell identification based on artificial neural network ensembles
352 Citations2002Zhi‐Hua Zhou, Yuan Jiang +2 more
An automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subjects to be diagnosed.
Artificial IntelligenceConcept learning and heuristic classification in weak-theory domains
255 Citations1990Bruce Porter, Ray Bareiss +1 more
This paper describes a successful approach to concept learning for heuristic classification that has been applied to the domain of clinical audiology and achieved a competence level equaling that of human experts and far surpassing that of other machine learning programs.
Lecture notes in computer scienceDiversity versus Quality in Classification Ensembles Based on Feature Selection
206 Citations2000Pádraig Cunningham, John G. Carney
It is argued that feature subset-selection has emerged as a useful technique for creating diversity in ensembles and this diversity needs to be monitored in the creation of the ensemble, and an entropy measure of the outputs of the Ensemble Diversity Index is proposed.
Pose invariant face recognition
136 Citations2002Fu Jie Huang, Zhi-Hua Zhou +2 more
A novel neural network architecture is described, which can recognize human faces with any view in a certain viewing angle range (from left 30 degrees to right 30 degrees out of plane rotation).
International Journal of ForecastingGraded forecasting using an array of bipolar predictions: application of probabilistic neural networks to a stock market index
134 Citations1998Steven H. Kim, Se Hak Chun
A presentation of a new architecture for graded forecasting using an arrayed probabilistic network (APN) and use of a “mistake chart” to compare the accuracy of learning systems against default performance based on a constant prediction are explored.
Improving rule-based systems through case-based reasoning
130 Citations1991Andrew R. Golding
A novel architecture is presented for combining rule-based and case-based reasoning, and the result that this performance was better than what could be achieved with the rules alone illustrates the capacity of the architecture to improve on the rule- based system it starts with.
IEEE Transactions on Signal ProcessingClassification of seismic signals by integrating ensembles of neural networks
120 Citations1998Y. Shimshoni, Nathan Intrator
An integrated classification machine (ICM) is presented, which is a hierarchy of artificial neural networks (ANNs) that are trained to classify the seismic waveforms, indicating that combining a collection of ensembles of ANNs can be used to handle complex high dimensional classification problems.
A case study on bagging, boosting and basic ensembles of neural networks for OCR
81 Citations2002Jianchang Mao
It is shown that although the boosting ensemble is slightly more accurate than thebasic and bagging ensembles at zero rejection rate, the advantage of the boosting training over the basic and baging ensemble quickly disappears as more patterns are rejected.
Ensemble methods for handwritten digit recognition
80 Citations2003Lars Kai Hansen, Christian Liisberg +1 more
It is concluded that it is possible to improve performance significantly by introducing moderate-size ensembles; in particular, a 20-25% improvement has been found.
Proceedings of International Conference on Neural Networks (ICNN'96)Face recognition using hybrid classifier systems
52 Citations2002Srinivas Gutta, Harry Wechsler
The results reported in this paper on automatic face recognition using the FERET database are encouraging when one considers that the size of the test bed is in excess of 350 subjects and the great variability of the database.
Expert Systems with ApplicationsData mining for financial prediction and trading: application to single and multiple markets
50 Citations2003Se‐Hak Chun, Steven H. Kim
This study investigated the implications for portfolio management using an implicit learning technique (neural nets) and an explicit approach (CBR) and found that a trading strategy coupled with a forecasting system offers the possibility for returns in excess of a passive buy-and-hold approach.
Lecture notes in computer scienceCase Representation Issues for Case-Based Reasoning from Ensemble Research
26 Citations2001Pádraig Cunningham, Gabriele Zenobi
It is shown that if diversity is emphasised in the development of the ensemble that the ensemble members appear to be local learners specializing in sub-domains of the problem space, which has implications for the practice of feature subset selection.
Expert Systems with ApplicationsImpact of momentum bias on forecasting through knowledge discovery techniques in the foreign exchange market
18 Citations2002Se‐Hak Chun
This paper investigates the impact of momentum bias on forecasting financial markets through knowledge discovery techniques using implicit knowledge representation (NNs) and case based reasoning (CBR).
Expert SystemsChaotic analysis of predictability versus knowledge discovery techniques: case study of the Polish stock market
15 Citations2002Se‐Hak Chun, Kyoung–Jae Kim +1 more
This work states that neural networks and other knowledge techniques have been applied extensively to the task of predicting financial variables and indicate that financial markets exhibit nonlinear dynamics in the form of chaotic behavior.
Expert SystemsAutomated generation of new knowledge to support managerial decision‐making: case study in forecasting a stock market
10 Citations2004Se‐Hak Chun, Steven H. Kim
This paper explores how the novel tools in the form of learning systems from artificial intelligence can support decision‐making in the ubiquitous managerial task of forecasting.