• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2299-2308
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• 1997

While most machine learning research has been primarily concerned with the development of systems that implement one type of learning strategy, we use a multistrategy approach which integrates rule induction learning and instance-based learning, and show how this marriage allows for overall better performance. In the rule induction learning phase, we derive an entropy function, based on Hellinger divergence, which can measure the amount of information each inductive rule contains, and show how well the Hellinger divergence measures the importance of each rule. We also propose some heuristics to reduce the computational complexity by analyzing the characteristics of the Hellinger measure. In the instance-based learning phase, we improve the current instance-based learning method in a number of ways. The system has been implemented and tested on a number of well-known machine learning data sets. The performance of the system has been compared with that of other classification learning technique.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.302-304
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• 2011

Rule based machine learning techniques are very important in our real world now. We can list out some important application which we can apply rule based machine learning algorithm such as medical data mining, business transaction mining. The different between rules based machine learning and model based machine learning is that model based machine learning out put some models, which often are very difficult to understand by expert or human. But rule based techniques output are the rule sets which is in IF THEN format. For example IF blood pressure=90 and kidney problem=yes then take this drug. By this way, medical doctor can easy modify and update some usable rule. This is the scenario in medical decision support system. Currently, Rough set is one of the most famous theory which can be used for produce the rule. LEM2 is the algorithm use this theory and can produce the small set of rule on the database. In this paper, we present an improvement of LEM2 algorithm which incorporates the variable precision techniques.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.241-248
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• 2007

In this paper, we propose a Transformation-Based Learning (TBL) method on generating the Korean standard pronunciation. Previous studies on the phonological processing have been focused on the phonological rule applications and the finite state automata (Johnson 1984; Kaplan and Kay 1994; Koskenniemi 1983; Bird 1995). In case of Korean computational phonology, some former researches have approached the phonological rule based pronunciation generation system (Lee et al. 2005; Lee 1998). This study suggests a corpus-based and data-oriented rule learning method on generating Korean standard pronunciation. In order to substituting rule-based generation with corpus-based one, an aligned corpus between an input and its pronunciation counterpart has been devised. We conducted an experiment on generating the standard pronunciation with the TBL algorithm, based on this aligned corpus.

• Journal of information and communication convergence engineering
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• v.14 no.2
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• pp.97-105
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• 2016

With the increasing demand for interoperability among existing learning resource systems in order to enable the sharing of learning resources, such resources need to be annotated with ontologies that use different metadata standards. These different ontologies must be reconciled through ontology mediation, so as to cope with information heterogeneity problems, such as semantic and structural conflicts. In this paper, we propose an ontology-mapping technique using Semantic Web Rule Language (SWRL) to generate semantic mapping rules that integrate learning resources from different systems and that cope with semantic and structural conflicts. Reasoning rules are defined to support a semantic search for heterogeneous learning resources, which are deduced by rule-based inference. Experimental results demonstrate that the proposed approach enables the integration of learning resources originating from multiple sources and helps users to search across heterogeneous learning resource systems.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1841-1848
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• 1999

DELVAUX is a genetics-based inductive learning system that learns a rule-set, which consists of Bayesian classification rules, from sets of examples for classification tasks. One problem that DELVAUX faces in the rule-set learning process is that, occasionally, the learning process ends with a local optimum without finding the best rule-set. Another problem is that, occasionally, the learning process ends with a rule-set that performs well for the training examples but not for the unknown testing examples. This paper describes efforts to alleviate these two problems centering on the N-version learning approach, in which multiple rule-sets are learning and a classification system is constructed with those learned rule-sets to improve the overall performance of a classification system. For the implementation of the N-version learning approach, we propose a decision-making scheme that can draw a decision using multiple rule-sets and a genetic algorithm approach to find a good combination of rule-sets from a set of learned rule-sets. We also present empirical results that evaluate the effect of the N-version learning approach in the DELVAUX learning environment.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.170-175
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• 2001

Q-learning is a kind of reinforcement learning where the agent solves the given task based on rewards received from the environment. Most research done in the field of Q-learning has focused on discrete domains, although the environment with which the agent must interact is generally continuous. Thus we need to devise some methods that enable Q-learning to be applicable to the continuous problem domain. In this paper, an extended fuzzy rule is proposed so that it can incorporate Q-learning. The interpolation technique, which is widely used in memory-based learning, is adopted to represent the appropriate Q value for current state and action pair in each extended fuzzy rule. The resulting structure based on the fuzzy inference system has the capability of solving the continuous state about the environment. The effectiveness of the proposed structure is shown through simulation on the cart-pole system.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.663-667
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• 2004

This paper presents new pruning and learning methods for the fuzzy rule-based classifier. The structure of the proposed classifier is framed from the fuzzy sets in the premise part of the rule and the Bayesian classifier in the consequent part. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the decision region to one for all feature values. For the improvement of the classification performance, the parameters of the proposed classifier are finely adjusted by using the gradient descent method so that the misclassified feature vectors are correctly re-categorized. The cost function is determined as the squared-error between the classifier output for the correct class and the sum of the maximum output for the rest and a positive scalar. Then, the learning rules are derived from forming the gradient. Finally, the fuzzy rule-based classifier is tested on two data sets and is found to demonstrate an excellent performance.

• ETRI Journal
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• v.15 no.2
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• pp.35-51
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• 1993

This paper presents function approximation based on nonparametric estimation. As an estimation model of function approximation, a three layered network composed of input, hidden and output layers is considered. The input and output layers have linear activation units while the hidden layer has nonlinear activation units or kernel functions which have the characteristics of bounds and locality. Using this type of network, a many-to-one function is synthesized over the domain of the input space by a number of kernel functions. In this network, we have to estimate the necessary number of kernel functions as well as the parameters associated with kernel functions. For this purpose, a new method of parameter estimation in which linear learning rule is applied between hidden and output layers while nonlinear (piecewise-linear) learning rule is applied between input and hidden layers, is considered. The linear learning rule updates the output weights between hidden and output layers based on the Linear Minimization of Mean Square Error (LMMSE) sense in the space of kernel functions while the nonlinear learning rule updates the parameters of kernel functions based on the gradient of the actual output of network with respect to the parameters (especially, the shape) of kernel functions. This approach of parameter adaptation provides near optimal values of the parameters associated with kernel functions in the sense of minimizing mean square error. As a result, the suggested nonparametric estimation provides an efficient way of function approximation from the view point of the number of kernel functions as well as learning speed.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.225-232
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• 2023

With the recent increase of multiple robots cooperating in smart manufacturing logistics environments, it has become very important how to predict the safety and efficiency of the individual tasks and dynamically assign them to the best one of available robots. In this paper, we propose a novel task policy learner based on deep relational reinforcement learning for predicting the safety and efficiency of tasks in a multi-robot manufacturing logistics environment. To reduce learning complexity, the proposed system divides the entire safety/efficiency prediction process into two distinct steps: the policy parameter estimation and the rule-based policy inference. It also makes full use of domain-specific knowledge for policy rule learning. Through experiments conducted with virtual dynamic manufacturing logistics environments using NVIDIA's Isaac simulator, we show the effectiveness and superiority of the proposed system.