Tianhua Chen

Exploiting Data Reliability and Fuzzy Clustering for Journal Ranking

Journal impact indicators are widely accepted as possible measurements of academic journal quality. However, much debate has recently surrounded their use, and alternative journal impact evaluation techniques are desirable. Aggregation of multiple indicators offers a promising method to produce a more robust ranking result, avoiding the possible bias caused by the use of a single impact indicator. In this paper, fuzzy aggregation and fuzzy clustering, especially the ordered weighted averaging (OWA) operators are exploited to aggregate the quality scores of academic journals that are obtained from different impact indicators. Also, a novel method for linguistic term-based fuzzy cluster grouping is proposed to rank academic journals. The paper allows for the construction of distinctive fuzzy clusters of academic journals on the basis of their performance with respect to different journal impact indicators, which may be subsequently combined via the use of the OWA operators. Journals are ranked in relation to their memberships in the resulting combined fuzzy clusters. In particular, the nearest-neighbor guided aggregation operators are adopted to characterize the reliability of the indicators, and the fuzzy clustering mechanism is utilized to enhance the interpretability of the underlying ranking procedure. The ranking results of academic journals from six subjects are systematically compared with the outlet ranking used by the Excellence in Research for Australia, demonstrating the significant potential of the proposed approach.

Induction of accurate and interpretable fuzzy rules from preliminary crisp representation

Abstract This paper proposes a novel approach for building transparent knowledge-based systems by generating accurate and interpretable fuzzy rules. The learning mechanism reported here induces fuzzy rules via making use of only predefined fuzzy labels that reflect prescribed notations and domain expertise, thereby ensuring transparency in the knowledge model adopted for problem solving. It works by mapping every coarsely learned crisp production rule in the knowledge base onto a set of potentially useful fuzzy rules, which serves as an initial step towards an intuitive technique for similarity-based rule generalisation. This is followed by a procedure that locally selects a compact subset of the emerging fuzzy rules, so that the resulting subset collectively generalises the underlying original crisp rule. The outcome of this local procedure forms the input to a global genetic search process, which seeks for a trade-off between accuracy and complexity of the eventually induced fuzzy rule base while maintaining transparency. Systematic experimental results are provided to demonstrate that the induced fuzzy knowledge base is of high performance and interpretability.

Ordered weighted aggregation of fuzzy similarity relations and its application to detecting water treatment plant malfunction

Abstract Ordered weighted aggregation procedures have been introduced in many applications with promising results. In this paper, an innovative approach for ordered weighted aggregation of fuzzy relations is proposed. It allows the integration of component relations generated from different perspectives of a certain observation to form an overall fuzzy relation, deriving a useful similarity measure for observed data points. Two types of aggregation are investigated: (a) min/max operators are employed for the aggregation of component relations defined by the minimum T-norm; and (b) sum/product operators are employed for the aggregation of component relations defined by the Łukasiewicz T -norm. The resultant ordered weighted aggregations prove to preserve the desirable reflexivity and symmetry properties, with T -transitivity also conditionally preserved if appropriate weighting vectors are adopted. The conditions upon which the proposed aggregated relations preserve T -transitivity are studied. The characteristics of applying an aggregated relation in combination with clustering procedures is also experimentally examined, where fuzzy similarity relations regarding individual features are aggregated to support hierarchical clustering. An application to the detection of water treatment plant malfunction demonstrates that better results can be obtained with the transitive fuzzy relations acting as the required similarity measures, as compared to the use of non-transitive ones. By introducing transitivity to the aggregation the interpretability of the detection system is also enriched.

Fuzzy rule weight modification with particle swarm optimisation

The most challenging problem in developing fuzzy rule-based classification systems is the construction of a fuzzy rule base for the target problem. In many practical applications, fuzzy sets that are of particular linguistic meanings, are often predefined by domain experts and required to be maintained in order to ensure interpretability of any subsequent inference results. However, learning fuzzy rules using fixed fuzzy quantity space without any qualification will restrict the accuracy of the resulting rules. Fortunately, adjusting the weights of fuzzy rules can help improve classification accuracy without degrading the interpretability. There have been different proposals for fuzzy rule weight tuning through the use of various heuristics with limited success. This paper proposes an alternative approach using Particle Swarm Optimisation in the search of a set of optimal rule weights, entailing high classification accuracy. Systematic experimental studies are carried out using common benchmark data sets, in comparison to popular rule based learning classifiers. The results demonstrate that the proposed approach can boost classification performance, especially when the size of the initially built rule base is relatively small, and is competitive to popular rule-based learning classifiers.

Nearest neighbour-guided induced OWA and its application to journal ranking

Aggregation operators are useful tools which summarise multiple inputs to a single output. In practice, inputs to such operators are variables which represent different criteria, measurements, or opinions from experts. In this paper, a nearest neighbour-guided induced OWA operator, abbreviated as kNN-IOWA, is proposed as a special case of the generic induced OWA where the input arguments are ordered by the average distances to their k nearest neighbours. The weighting vectors in kNN-IOWA are defined, which are used to interpret the overall behaviour of the operators reliability. kNN-IOWA is applied for building aggregated fuzzy relations between academic journals, based on their indicator scores. It combines the similarities between academic journals to assess their performance with respect to different journal impact indicators. The work is compared against different types of aggregation operator and tested on six bibliometric datasets. The results of experimental evaluation demonstrate that kNN-IOWA outperforms other aggregation operators in terms of standard accuracy and within-1 accuracy. The proposed method also exhibits the advantages of being more intuitive and interpretable.

Effective Diagnosis of Diabetes with a Decision Tree-Initialised Neuro-fuzzy Approach

Diabetes mellitus is a serious hazard to human health that can result in a number of severe complications. Early diagnosis and treatment is of significant importance to patients for the acquisition of a better quality life and precaution against subsequent complications. This paper proposes an approach by learning a fuzzy rule base for the effective diagnosis of diabetes mellitus. In particular, the proposed approach starts with the generation of a crisp rule base through a decision tree learning mechanism, which is data-driven and able to learn simple rule structures. The crisp rule base is then transformed into a fuzzy rule base, which forms the input to the powerful neuro-fuzzy framework of ANFIS, further optimising the parameters of both rule antecedents and consequents. Experimental study on the well-known Pima Indian diabetes data set is provided to demonstrate the promising potential of the proposed approach.

Fuzzy rough feature selection based on OWA aggregation of fuzzy relations

The interaction between features, or attributes, of a dataset forms a major topic in machine learning and data mining. In particular, a wide range of methods have been established for feature selection, ranking, and grouping. Amongst these, fuzzy rough set based feature selection (FRFS) has been shown to be highly effective at reducing dimensionality for real-valued datasets while retaining attribute semantics. In fuzzy rough sets, the concept of crisp equivalence classes is extended by fuzzy similarity relations, and real-valued similarity measures can be captured between data instances in terms of their attribute values. Therefore, it is desirable to study the aggregation of fuzzy similarity relations to reflect the interactions between attributes. This paper presents an approach that employs OWA aggregation of fuzzy similarity relations to better perform FRFS. A high degree of modelling flexibility is provided by choosing the stress function in OWA. Experimental studies demonstrate that through using different stress functions, different features may be selected; and that given an appropriate stress function, the quality of selected features can improve over that achievable by the state-of-the-art FRFS, in performing classification tasks.

Reliability-guided fuzzy classifier ensemble

Classifier ensembles form an important approach to improving classification performance. As such, there have been different proposals made in the literature that provide a range of means to construct and aggregate classifier ensembles. However, the resulting systems may contain unreliable members with false or biased judgements in the ensemble. The removal of unreliable members is necessary to optimise the overall performance of such systems. Smaller ensembles involving reduced ensemble members also helps relax the requirement of computational memory, thereby strengthening the systems run-time efficiency. To differentiate the potential contributions of different ensemble members while reducing the adverse impact of any unreliable judgement upon the system, a nearest neighbour-based reliability measure is incorporated into the process of classifier ensemble selection. In particular, reliabilities of selected ensemble members are perceived as a stress function, from which argument-dependent weights are heuristically generated for final aggregated decision. Experimental investigations are carried out, demonstrating the efficacy of the proposed approach, where fuzzy classifiers are utilised as base members of the emerging ensemble.

Induction of quantified fuzzy rules with Particle Swarm Optimisation

The use of fuzzy quantifiers to modify the fuzzy linguistic terms in fuzzy models helps build fuzzy systems in a more natural way, by capturing finer pieces of information embedded in the training data. This paper presents a practical approach for the acquisition of fuzzy production rules with quantifiers, based on a class-dependent simultaneous rule learning strategy where each class is associated with a subset of descriptive rules. It is implemented by particle swam optimisation. The performance of the learned fuzzy rules with and without fuzzy quantifiers is evaluated on various UCI benchmark data sets, in comparison to popular alternative rule based learning classifiers. Experimental results demonstrate that rule bases generated by the proposed approach indeed boost classification performance as compared to those involving no fuzzy quantifiers, with at least competitive performance to the alternative learning classifiers.

Refinement of fuzzy rule weights with particle swarm optimisation

The most challenging problem in the design of fuzzy rule-based classification systems is the construction of a fuzzy rule base for the target problem. Much research has focused on generating and adjusting antecedent fuzzy sets. In many cases, initial fuzzy sets, each of which has a linguistic meaning, are predefined by domain experts and are thus required to be maintained in order to ensure interpretability of any subsequent inference results. However, learning fuzzy rules using fixed fuzzy quantity space without any quantification will restrict the accuracy of the resulting rules. Fortunately, adjusting the weight of a fuzzy if-then rule may help improve classification accuracy without degrading the interpretability. There have been different proposals for fuzzy rule weight tuning through the use of various heuristics with limited success. This paper proposes an alternative approach using Particle Swarm Optimisation in the search of a set of optimal rule weights, which can entail high classification accuracy. The proposed method is initially tested on the iris data set with regard to different predefined fuzzy partitions of linguistic variables to assess its performance. Experimental results demonstrate that the proposed approach is not sensitive to the predefined fuzzy partitions, and can boost classification performance especially when a coarse fuzzy partition is given.