33,340 research outputs found
Fuzzy natural language similarity measures through computing with words
A vibrant area of research is the understanding of human language by machines to engage in
conversation with humans to achieve set goals. Human language is naturally fuzzy by nature,
with words meaning different things to different people, depending on the context. Fuzzy
words are words with a subjective meaning, typically used in everyday human natural
language dialogue and often ambiguous and vague in meaning and dependent on an
individualâs perception. Fuzzy Sentence Similarity Measures (FSSM) are algorithms that can
compare two or more short texts which contain fuzzy words and return a numeric measure
of similarity of meaning between them.
The motivation for this research is to create a new FSSM called FUSE (FUzzy Similarity
mEasure). FUSE is an ontology-based similarity measure that uses Interval Type-2 Fuzzy Sets
to model relationships between categories of human perception-based words. Four versions
of FUSE (FUSE_1.0 â FUSE_4.0) have been developed, investigating the presence of linguistic
hedges, the expansion of fuzzy categories and their use in natural language, incorporating
logical operators such as ânotâ and the introduction of the fuzzy influence factor.
FUSE has been compared to several state-of-the-art, traditional semantic similarity measures
(SSMâs) which do not consider the presence of fuzzy words. FUSE has also been compared to
the only published FSSM, FAST (Fuzzy Algorithm for Similarity Testing), which has a limited
dictionary of fuzzy words and uses Type-1 Fuzzy Sets to model relationships between
categories of human perception-based words. Results have shown FUSE is able to improve on
the limitations of traditional SSMâs and the FAST algorithm by achieving a higher correlation
with the average human rating (AHR) compared to traditional SSMâs and FAST using several
published and gold-standard datasets.
To validate FUSE, in the context of a real-world application, versions of the algorithm were
incorporated into a simple Question & Answer (Q&A) dialogue system (DS), referred to as
FUSION, to evaluate the improvement of natural language understanding. FUSION was tested
on two different scenarios using human participants and results compared to a traditional
SSM known as STASIS. Results of the DS experiments showed a True rating of 88.65%
compared to STASIS with an average True rating of 61.36%. Results showed that the FUSE
algorithm can be used within real world applications and evaluation of the DS showed an
improvement of natural language understanding, allowing semantic similarity to be
calculated more accurately from natural user responses.
The key contributions of this work can be summarised as follows: The development of a new
methodology to model fuzzy words using Interval Type-2 fuzzy sets; leading to the creation of
a fuzzy dictionary for nine fuzzy categories, a useful resource which can be used by other
researchers in the field of natural language processing and Computing with Words with other
fuzzy applications such as semantic clustering. The development of a FSSM known as FUSE,
which was expanded over four versions, investigating the incorporation of linguistic hedges,
the expansion of fuzzy categories and their use in natural language, inclusion of logical
operators such as ânotâ and the introduction of the fuzzy influence factor. Integration of the
FUSE algorithm into a simple Q&A DS referred to as FUSION demonstrated that FSSM can be
used in a real-world practical implementation, therefore making FUSE and its fuzzy dictionary
generalisable to other applications
A new fuzzy set merging technique using inclusion-based fuzzy clustering
This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes. The merger method is applied to fuzzy rule base simplification by automatically replacing the fuzzy sets corresponding to a given cluster with that pertaining to cluster prototype. The feasibility and the performance of the proposed method are studied using an application in mobile robot navigation. The results indicate that the proposed merging and rule base simplification approach leads to good navigation performance in the application considered and to fuzzy models that are interpretable by experts. In this paper, we concentrate mainly on fuzzy systems with Gaussian membership functions, but the general approach can also be applied to other parameterized fuzzy sets
Extending Similarity Measures of Interval Type-2 Fuzzy Sets to General Type-2 Fuzzy Sets
Similarity measures provide one of the core tools that enable reasoning about
fuzzy sets. While many types of similarity measures exist for type-1 and
interval type-2 fuzzy sets, there are very few similarity measures that enable
the comparison of general type-2 fuzzy sets. In this paper, we introduce a
general method for extending existing interval type-2 similarity measures to
similarity measures for general type-2 fuzzy sets. Specifically, we show how
similarity measures for interval type-2 fuzzy sets can be employed in
conjunction with the zSlices based general type-2 representation for fuzzy sets
to provide measures of similarity which preserve all the common properties
(i.e. reflexivity, symmetry, transitivity and overlapping) of the original
interval type-2 similarity measure. We demonstrate examples of such extended
fuzzy measures and provide comparisons between (different types of) interval
and general type-2 fuzzy measures.Comment: International Conference on Fuzzy Systems 2013 (Fuzz-IEEE 2013
A kernel-based framework for learning graded relations from data
Driven by a large number of potential applications in areas like
bioinformatics, information retrieval and social network analysis, the problem
setting of inferring relations between pairs of data objects has recently been
investigated quite intensively in the machine learning community. To this end,
current approaches typically consider datasets containing crisp relations, so
that standard classification methods can be adopted. However, relations between
objects like similarities and preferences are often expressed in a graded
manner in real-world applications. A general kernel-based framework for
learning relations from data is introduced here. It extends existing approaches
because both crisp and graded relations are considered, and it unifies existing
approaches because different types of graded relations can be modeled,
including symmetric and reciprocal relations. This framework establishes
important links between recent developments in fuzzy set theory and machine
learning. Its usefulness is demonstrated through various experiments on
synthetic and real-world data.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Automatic histogram threshold using fuzzy measures
In this paper, an automatic histogram threshold approach based on a fuzziness measure is presented. This work is an improvement of an existing method. Using fuzzy logic concepts, the problems involved in finding the minimum of a criterion function are avoided. Similarity between gray levels is the key to find an optimal threshold. Two initial regions of gray levels, located at the boundaries of the histogram, are defined. Then, using an index of fuzziness, a similarity process is started to find the threshold point. A significant contrast between objects and background is assumed. Previous histogram equalization is used in small contrast images. No prior knowledge of the image is required.info:eu-repo/semantics/publishedVersio
Extended Fuzzy Clustering Algorithms
Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. Ithas been applied successfully in various fields including finance and marketing. Despitethe successful applications, there are a number of issues that must be dealt with in practicalapplications of fuzzy clustering algorithms. This technical report proposes two extensionsto the objective function based fuzzy clustering for dealing with these issues. First, the(point) prototypes are extended to hypervolumes whose size is determined automaticallyfrom the data being clustered. These prototypes are shown to be less sensitive to a biasin the distribution of the data. Second, cluster merging by assessing the similarity amongthe clusters during optimization is introduced. Starting with an over-estimated number ofclusters in the data, similar clusters are merged during clustering in order to obtain a suitablepartitioning of the data. An adaptive threshold for merging is introduced. The proposedextensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resultingextended algorithms are given. The properties of the new algorithms are illustrated invarious examples.fuzzy clustering;cluster merging;similarity;volume prototypes
Techniques for clustering gene expression data
Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognises these limitations and implements procedures to overcome them. It provides a framework for the evaluation of clustering in gene expression analyses. The nature of microarray data is discussed briefly. Selected examples are presented for the clustering methods considered
An artificial immune systems based predictive modelling approach for the multi-objective elicitation of Mamdani fuzzy rules: a special application to modelling alloys
In this paper, a systematic multi-objective Mamdani fuzzy modeling approach is proposed, which can be viewed as an extended version of the previously proposed Singleton fuzzy modeling paradigm. A set of new back-error propagation (BEP) updating formulas are derived so that they can replace the old set developed in the singleton version. With the substitution, the extension to the multi-objective Mamdani Fuzzy Rule-Based Systems (FRBS) is almost endemic. Due to the carefully chosen output membership functions, the inference and the defuzzification methods, a closed form integral can be deducted for the defuzzification method, which ensures the efficiency of the developed Mamdani FRBS. Some important factors, such as the variable length coding scheme and the rule alignment, are also discussed. Experimental results for a real data set from the steel industry suggest that the proposed approach is capable of eliciting not only accurate but also transparent FRBS with good generalization ability
- âŠ