Fuzzy Inductive Learning Research Articles

RULES-F: A fuzzy inductive learning algorithm

Current inductive learning algorithms have difficulties handling attributes with numerical values. This paper presents RULES-F, a new fuzzy inductive learning algorithm in the RULES family, which integrates the capabilities and performance of a good inductive learning algorithm for classification applications with the ability to create accurate and compact fuzzy models for the generation of numerical outputs. The performance of RULES-F in two simulated control applications involving numerical output parameters is demonstrated and compared with that of the well-known fuzzy rule induction algorithm by Wang and Mendel.

사용자 행동 패턴 선호도 학습을 위한 퍼지 귀납 학습 시스템

본 논문은 스마트 홈과 같이 다양한 센서 및 제어 네트워크가 밀집되어 있는 유비쿼터스 환경 하에서 복잡한 인터페이스의 사용에 대한 사용자의 인지 부담(cognitive load)을 줄이고, 개인화된(personalized) 서비스를 자율적으로 제공하기 위한 새로운 사용자 행동 패턴 선호도 학습기법을 제안하였다. 이를 위해 지식 발견(knowledge discovery)을 위한 평생 학습(life-long learning)의 관점에서 퍼지귀납(fuzzy inductive) 학습 방법론을 제안하며, 이것은 수치 데이터로부터 입력 공간에 대한 효율적인 퍼지 분할(fuzzy partition)을 얻어내고 일관성 있는(consistent) 퍼지 상관 롤(fuzzy association rule)을 얻어내도록 한다. Smart home is one of the ubiquitous environment platforms with various complex sensor-and-control network. In this paper, a now learning methodology for learning user's behavior preference pattern is proposed in the sense of reductive user's cognitive load to access complex interfaces and providing personalized services. We propose a fuzzy inductive learning methodology based on life-long learning paradigm for knowledge discovery, which tries to construct efficient fuzzy partition for each input space and to extract fuzzy association rules from the numerical data pattern.

MFILM: a multi-dimensional fuzzy inductive learning method

Inductive learning that creates a decision tree from a set of existing examples is shown to be useful for automated knowledge acquisition. Most of the existing methods however, handle only single-dimensional decision problems. Only some methods can deal with multi-dimensional decision problems. However, they are based on crisp concepts that are weak in handling marginal cases. In this paper, we present a multi-dimensional fuzzy inductive learning method that integrates the fuzzy set theory into the conventional multi-dimensional decision tree induction methods. The method converts a multi-dimensional decision tree into a fuzzy multi-dimensional decision tree in which hurdle values for splitting branches and classes associated with leaves are fuzzy. Results from empirical tests indicate that the new fuzzy approach outperforms the other conventional methods.

An e-mail analysis method based on text mining techniques

This paper proposes a method employing text mining techniques to analyze e-mails collected at a customer center. The method uses two kinds of domain-dependent knowledge. One is a key concept dictionary manually provided by human experts. The other is a concept relation dictionary automatically acquired by a fuzzy inductive learning algorithm. The method inputs the subject and the body of an e-mail and decides a text class for the e-mail. Also, the method extracts key concepts from e-mails and presents their statistical information. This paper applies the method to three kinds of analysis tasks: a product analysis task, a contents analysis task, and an address analysis task. The results of numerical experiments indicate that acquired concept relation dictionaries correspond to the intuition of operators in the customer center and give highly precise ratios in the classification.

Intelligent condition monitoring using fuzzy inductive learning

Extensive research has been performed for developing knowledge based intelligent monitoring systems for improving the reliability of manufacturing processes. Due to the high expense of obtaining knowledge from human experts, it is expected to develop new techniques to obtain the knowledge automatically from the collected data using data mining techniques. Inductive learning has become one of the widely used data mining methods for generating decision rules from data. In order to deal with the noise or uncertainties existing in the data collected in industrial processes and systems, this paper presents a new method using fuzzy logic techniques to improve the performance of the classical inductive learning approach. The proposed approach, in contrast to classical inductive learning method using hard cut point to discretize the continuous-valued attributes, uses soft discretization to enable the systems have less sensitivity to the uncertainties and noise. The effectiveness of the proposed approach has been illustrated in an application of monitoring the machining conditions in uncertain environment. Experimental results show that this new fuzzy inductive learning method gives improved accuracy compared with using classical inductive learning techniques.

A fuzzy inductive learning algorithm for a text mining system

A fuzzy inductive learning algorithm for a text mining system

ファジィ帰納学習におけるキー概念集合を含む属性値の扱い

ファジィ帰納学習におけるキー概念集合を含む属性値の扱い

ファジィ帰納学習法の発展と今後の展開

コンピュータ及びネットワーク環境が普及し、データの電子化が加速する一方、これらのデータに内在する知識を発見する帰納学習法の重要性が高まっている。本論文では、決定木形式の判断規則の集合を学習する代表的な帰納学習法ID3を中心として、提案されている他の帰納学習法及びファジィ帰納学習法を紹介し、各手法間の関連を明らかにする。また、関連技術の研究動向を紹介し、ファジィ帰納学習法の今後の研究の方向性を明らかにする。

Description of learning space based on interval fuzzy sets and fuzzy inductive learning algorithm ILA

In this paper, based on interval fuzzy sets, the inductive learning space is fuzzified, the distinction interval is newly defined to measure the definability of concepts to be learned. By applying the fining operation of partitions for the basic event space, the value of the distinction interval and the efficiency of inductive learning for concepts to be learned are improved. Based on these, fuzzy Inductive Learning Algorithm ILA is presented for fuzzy inductive learning problems. In terms of the example sets of concepts given by experts, algorithm ILA can learn fuzzy inference rules about concepts to be learned. Finally, the complexity of algorithm ILA is also discussed.

A fuzzy inductive learning strategy for modular rules

In real applications, data provided to a learning system usually contain linguistic information which greatly influences concept descriptions derived by conventional inductive learning methods. The design of learning methods to learn concept descriptions in working with vague data is thus very important. In this paper, we apply fuzzy set concepts to machine learning to solve this problem. A fuzzy learning algorithm based on the maximum information gain is proposed to manage linguistic information. The proposed learning algorithm generates fuzzy rules from “soft” instances, which differ from conventional instances in that they have class membership values. Experiments on the Sports and the Iris Flower classification problems are presented to compare the accuracy of the proposed algorithm with those of some other learning algorithms. Experimental results show that the rules derived from our approach are simpler and yield higher accuracy than those from some other learning algorithms.

探索型ファジィ帰納学習による診断知識の学習

探索型ファジィ帰納学習による診断知識の学習

ファジィ帰納学習による数値予測規則の学習

ファジィ帰納学習による数値予測規則の学習

FILM: a fuzzy inductive learning method for automated knowledge acquisition

Inductive learning that creates decision trees from a set of existing cases is useful for automated knowledge acquisition. Most of the existing methods in literature are based on crisp concepts that are weak in handling marginal cases. In this paper, we present a fuzzy inductive learning method that integrates the fuzzy set theory into the regular inductive learning processes. The method converts a decision tree induced from regular method into a fuzzy decision tree in which hurdle values for splitting branches and classes associated with leaves are fuzzy. Results from empirical tests indicate that the new fuzzy approach outperforms the popular discriminant analysis and ID3 in predictive accuracy.

ファジイ帰納学習アルゴリズムの改良

ファジイ帰納学習アルゴリズムの改良