Myung Won Kim

Efficient fuzzy rule generation based on fuzzy decision tree for data mining

In this paper, we propose an efficient fuzzy rule generation algorithm based on fuzzy decision tree for data mining. We combine the comprehensibility of rules generated based on decision tree such as ID3 and C4.5 and the expressive power of fuzzy sets. Particularly, fuzzy rules allow us to effectively classify patterns of nonaxis-parallel decision boundaries, which are difficult to do using attribute-based classification methods. In our algorithm we first determine an appropriate set of membership functions for each attribute of data using histogram analysis. Given a set of membership functions then we construct a fuzzy decision tree in a similar way to that of ID3 and C4.5. We also apply the genetic algorithm to tune the initial set of membership functions. We have experimented our algorithm with several benchmark data sets. The experiment results show that our method is more efficient in performance and comprehensibility of rules compared with other methods including C4.5.

A Collaborative Recommendation Based on Neural Networks

Collaborative filtering is one of the widely used methods for recommendation. It recommends an item to a user based on the reference users’ preferences for the target item or the target user’s preferences for the reference items. In this paper, we propose a neural network based collaborative filtering method. Our method builds a model by learning correlation between users or items using a multi-layer perceptron. We also investigate selection of the reference users or items based on similarity to improve performance. We finally demonstrate that our method outperforms the existing methods through experiments using the EachMovie data.

Optimized fuzzy classification using genetic algorithm

Fuzzy rules are suitable for describing uncertain phenomena and natural for human understanding and they are, in general, efficient for classification. In addition, fuzzy rules allow us to effectively classify data having non-axis-parallel decision boundaries, which is difficult for the conventional attribute-based methods. In this paper, we propose an optimized fuzzy rule generation method for classification both in accuracy and comprehensibility (or rule complexity). We investigate the use of genetic algorithm to determine an optimal set of membership functions for quantitative data. In our method, for a given set of membership functions a fuzzy decision tree is constructed and its accuracy and rule complexity are evaluated, which are combined into the fitness function to be optimized. We have experimented our algorithm with several benchmark data sets. The experiment results show that our method is more efficient in performance and comprehensibility of rules compared with the existing methods including C4.5 and FID3.1 (Fuzzy ID3).

Efficient Fuzzy Rules For Classification

Fuzzy rules are suitable for describing uncertain phenomena and natural for human understanding and they are, in general, efficient for classification. In this paper, we propose an optimized fuzzy rule generation method for classification both in accuracy and comprehensibility (or rule complexity). We investigate the use of genetic algorithm to determine an optimal set of membership functions for quantitative data. In our method, for a given set of membership functions a fuzzy decision tree is constructed and its accuracy and rule complexity are evaluated, which are combined into the fitness function to be optimized. We have experimented our algorithm with several benchmark data sets. The experiment results show that our method is more efficient in performance and compactness of rules compared with the existing methods

An Efficient Coevolutionary Algorithm Using Dynamic Species Control

A coevolutionary algorithm is an extention of the conventional genetic algorithm that incorporates the strategy of divide and conquer in developing a complex solution in the form of interacting co-adapted subcomponents. In this paper we propose an efficient coevolutionary algorithm dynamically controlling species splitting and merging. Our algorithm conducts efficient local search in the reduced search space by splitting species for independent variables while it conducts global search by merging species for interdependent variables. We have experimented the proposed algorithm with some benchmarking function optimization problems and the inventory control problem, and have shown that the algorithm outperforms the existing coevolutionary algorithms.

Speech recognition with multi-modal features based on neural networks

Recent researches have been focusing on fusion of audio and visual features for reliable speech recognition in noisy environments. In this paper, we propose a neural network based model of robust speech recognition by integrating audio, visual, and contextual information. Bimodal Neural Network (BMNN) is a multi-layer perceptron of 4 layers, which combines audio and visual features of speech to compensate loss of audio information caused by noise. In order to improve the accuracy of speech recognition in noisy environments, we also propose a post-processing based on contextual information which are sequential patterns of words spoken by a user. Our experimental results show that our model outperforms any single mode models. Particularly, when we use the contextual information, we can obtain over 90% recognition accuracy even in noisy environments, which is a significant improvement compared with the state of art in speech recognition.

Optimized fuzzy decision tree using genetic algorithm

Fuzzy rules are suitable for describing uncertain phenomena and natural for human understanding and they are, in general, efficient for classification. In addition, fuzzy rules allow us to effectively classify data having non-axis-parallel decision boundaries, which is difficult for the conventional attribute-based methods. In this paper, we propose an optimized fuzzy rule generation method for classification both in accuracy and comprehensibility (or rule complexity). We investigate the use of genetic algorithm to determine an optimal set of membership functions for quantitative data. In our method, for a given set of membership functions a fuzzy decision tree is constructed and its accuracy and rule complexity are evaluated, which are combined into the fitness function to be optimized. We have experimented our algorithm with several benchmark data sets. The experiment results show that our method is more efficient in performance and complexity of rules compared with the existing methods.

Speech recognition by integrating audio, visual and contextual features based on neural networks

Recent researches have been focusing on fusion of audio and visual features for reliable speech recognition in noisy environments. In this paper, we propose a neural network based model of robust speech recognition by integrating audio, visual, and contextual information. Bimodal Neural Network (BMNN) is a multi-layer perceptron of 4 layers, which combines audio and visual features of speech to compensate loss of audio information caused by noise. In order to improve the accuracy of speech recognition in noisy environments, we also propose a post-processing based on contextual information which are sequential patterns of words spoken by a user. Our experimental results show that our model outperforms any single mode models. Particularly, when we use the contextual information, we can obtain over 90% recognition accuracy even in noisy environments, which is a significant improvement compared with the state of art in speech recognition.

Optimized fuzzy classification for data mining

F uzzy rules are suitable for describing uncertain phenomena and natural for human understanding and they are, in general, efficient for classification. In addition, fuzzy rules allow us to effectively classify data having non-axis-parallel decision boundaries, which is difficult for the conventional attribute-based methods. In this paper, we propose an optimized fuzzy rule generation method for classification both in accuracy and comprehensibility (or rule complexity). We investigate the use of genetic algorithm to determine an optimal set of membership functions for quantitative data. In our method, for a given set of membership functions a fuzzy decision tree is constructed and its accuracy and rule complexity are evaluated, which are combined into the fitness function to be optimized. We have experimented our algorithm with several benchmark data sets. The experiment results show that our method is more efficient in performance and comprehensibility of rules compared with the existing methods including C4.5 and FID3.1.

Optimization of fuzzy rules for classification using genetic algorithm

In this paper, we propose an efficient fuzzy rule generation algorithm based on fuzzy decision tree for high accuracy and better comprehensibility. We combine the comprehensibility of rules generated based on decision tree such as ID3 and C4.5 and the expressive power of fuzzy sets for dealing with quantitative data. Particularly, fuzzy rules allow us to effectively classify patterns of non-axis-parallel decision boundaries, which are difficult to do using attribute-based classification methods. We also investigate the use of genetic algorithm to optimize fuzzy decision trees in accuracy and comprehensibility by determining an appropriate set of membership functions for quantitative data. We have experimented our algorithm with several benchmark test data including manually generated two-class patterns, the iris data, the Wisconsin breast cancer data, and the credit screening data. The experiment results show that our method is more efficient in performance and comprehensibility of rules compared with methods including C4.5 and FID (Fuzzy ID3).