Ching-Hung Wang

Integrating fuzzy knowledge by genetic algorithms

We propose a genetic algorithm-based fuzzy knowledge integration framework that can simultaneously integrate multiple fuzzy rule sets and their membership function sets. The proposed approach consists of two phases: fuzzy knowledge encoding and fuzzy knowledge integration. In the encoding phase, each fuzzy rule set with its associated membership functions is first transformed into an intermediary representation and then further encoded as a string. The combined strings form an initial knowledge population, which is then ready for integration. In the knowledge-integration phase, a genetic algorithm is used to generate an optimal or nearly optimal set of fuzzy rules and membership functions from the initial knowledge population. Two application domains, the hepatitis diagnosis and the sugarcane breeding prediction, were used to show the performance of the proposed knowledge-integration approach. Results show that the fuzzy knowledge base derived using our approach performs better than every individual knowledge base.

A fuzzy inductive learning strategy for modular rules

Abstract 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.

Integrating membership functions and fuzzy rule sets from multiple knowledge sources

In this paper, we propose a GA-based fuzzy knowledge-integration framework that can simultaneously integrate multiple fuzzy rule sets and their membership function sets. The proposed two-phase approach includes fuzzy knowledge encoding and fuzzy knowledge integration. In the encoding phase, each fuzzy rule set with its associated membership functions is first transformed into an intermediary representation, and further encoded as a string. The combined strings form an initial knowledge population, which is then ready for integration. In the knowledge-integration phase, a genetic algorithm is used to generate an optimal or nearly optimal set of fuzzy rules and membership functions from the initial knowledge population. The hepatitis diagnostic problem was used to show the performance of the proposed knowledge-integration approach. Results show that the fuzzy knowledge-base resulting from using our approach performs better than every individual knowledge base.

Automatically integrating multiple rule sets in a distributed-knowledge environment

An actual knowledge application is made by means of evolution paradigms in terms of knowledge acquisition. An automatic knowledge integration approach in a distributed-knowledge environment is thus proposed to integrate multiple rule sets into a single effective rule set. The proposed approach consists of two phases: knowledge encoding and knowledge integration. In the encoding phase, each knowledge input is translated and expressed as a rule set, then encoded as a bit string. The combined bit strings from multiple knowledge inputs form an initial knowledge population, which is then ready for integration. In the knowledge integration phase, a genetic search technique generates an optimal or nearly optimal rule set from these initial knowledge-input strings. Finally, experimental results from diagnosis of brain tumors show that the rule set derived by the proposed approach is much more accurate than each initial rule set.

Inductive learning from fuzzy examples

In real applications, data provided to a learning system usually contain fuzzy information which greatly influences concept descriptions derived by conventional inductive learning methods. Modifying learning methods to learn concept descriptions in vague environments is thus very important. In this paper, we apply fuzzy set concept to machine learning to solve this problem. A fuzzy learning algorithm based on the version space strategy is proposed to manage fuzzy information. The proposed algorithm induces fuzzy linguistic inference rules from fuzzy instances, and finally infers outputs based on the fuzzy rules derived and user inputs. The Iris flower classification problem is used to compare the accuracy of the proposed algorithm with that of some other learning algorithms. Experimental results show that our method yields high accuracy.

Self-integrating knowledge-based brain tumor diagnostic system

Abstract In this paper, we present a self-integrating knowledge-based expert system for brain tumor diagnosis. The system we propose comprises knowledge building, knowledge inference and knowledge refinement. During knowlege building, an automatic knowledge-integration process, based on Darwins theory of natural selection, integrates knowledge derived from knowledge-acquisition tools and machine-learning methods to construct an initial knowledge base, thus eliminating a major bottleneck in developing a brain tumor diagnostic system. During the knowledge inference process, an inference engine exploits rules in the knowledge base to help diagnosticians determine brain tumor etiologies according to computer tomography pictures. And, a simple knowledge refinement method is proposed to modify the existing knowledge base during inference, which dramatically improves the accuracy of the derived rules. The performance of the brain tumor diagnostic system has been evaluated on actual brain tumor cases.

Fuzzy Inductive Learning Strategies

In real applications, data provided to a learning system usually contain linguistic information which greatly influences concept descriptions derived by conventional inductive learning methods. Design of learning methods for 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 AQR learning strategy is proposed to manage linguistic information. The proposed learning algorithm generates fuzzy linguistic rules from “soft” instances. 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.

A coverage-based genetic knowledge-integration strategy

Abstract In this paper, we propose a coverage-based genetic knowledge-integration approach to effectively integrate multiple rule sets into a centralized knowledge base. The proposed approach consists of two phases: knowledge encoding and knowledge integration. In the knowledge-encoding phase, each rule in the various rule sets that are derived from different sources (such as expert knowledge or existing knowledge bases) is first translated and encoded as a fixed-length bit string. The bit strings combined together thus form an initial knowledge population. In the knowledge-integration phase, a genetic algorithm applies genetic operations and credit assignment at each rule-string to generate an optimal or nearly optimal rule set. Experiments on diagnosing brain tumors were made to compare the accuracy of a rule set generated by the proposed approach with that of the initial rule sets derived from different groups of experts or induced by various machine learning techniques. Results show that the rule set derived by the proposed approach is more accurate than each initial rule set on its own.

FILSMR: 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 linguistic environments is thus very important. 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. Experiments on the sport classification problem are to demonstrate the effectiveness of the proposed algorithm. Experimental results show that the rules derived from our approach are simpler and yields high accuracy.

Integrating multiple rule sets by genetic algorithms

We propose a competition-based knowledge integration approach to effectively integrate multiple rule sets into a centralized knowledge base. The proposed approach consists of two phases: knowledge encoding and knowledge integrating. In the encoding phase, each rule in the rule set is first encoded as a rule bit-string. The combined bit strings from multiple rule sets thus form an initial knowledge population. In the knowledge integration phase, a genetic algorithm generates an optimal or nearly optimal rule set from these initial rule sets. Experiments on diagnosing brain tumors were made to compare the accuracy of a rule set generated by the proposed approach with that of the initial rule sets derived from different groups of experts or induced by various machine learning techniques. Results show that the rule set derived by the proposed approach is much more accurate than each initial rule set on its own.