Takahiro Akabane

On the Generalization of Grey Relational Analysis

In this paper we developed new grey relational analysis by expanding a range of treatable value. The classical GRA procedure deals with almost whitened values, reference vectors (or sequences), inspected vectors and grey relational grades. The classical GRA is called White-In-White-Out (WIWO) GRA in this paper. We developed two kinds of GRA algorithm based on the topological-based GRA, after defined several new operations of the interval grey number. On the one hand, GRA which provides the grey relational grade a whitened value finally is named Grey-In-White-Out (GIWO) GRA. On the other hand, GRA which provides the grey relational grade the interval grey number is named Grey-In-Grey-Out (GIGO) GRA. Both new GRA procedures deal with the reference vector and inspected vectors as the interval grey number. Three examples are given in this paper; the properties of the grey relational grade given by the proposals are discussed.

Decision rule extraction and reduction based on grey lattice classification

This paper proposes a decision rule of extraction and reduction that is based on grey lattice classification. This proposal method comes from joining between rough set theory and grey theory as an approximation algorithm. Grey lattice operations are defined by combining interval grey number in grey theory with interval lattice operations in interval algebra. By defining the equivalents in interval grey number, given data space is correspondent to equivalents of rough set. This proposal method classifies each data set into 3-patterns from given training samples, as existing possibility class, newly made possibility class and existing necessity class. As given examples which require only necessity class, decision rule is simplified by a reduction procedure.

A Realization Algorithm of Grey Structural Modeling with MATLAB

Grey structural modeling (GSM in short) is a new approach of system modeling methods succeeding to ISM and FSM. GSM has two procedures: estimating a hierarchy of given elements, estimating paths among given elements. The former procedure is constructed from complex equations. In this paper we developed one realization algorithm of the GSM procedure. The main problem we should solve is how to group given elements into several classes and to decide their hierarchy. We are possible to group analyzing an error matrix which is obtained from the localized grey relational grade, and we are also possible to decide their hierarchy according to the localized grey relational grade. We used the topological-based grey relational analysis. These procedures are shown as a pseudo language with several figures, and are realized by MATLAB. Several examples applied with the developed program are shown in this paper

A K-means Clustering Approach Based on Grey Theory

A lot of clustering algorithms based on grey system theory, especially based on the grey relational matrix, have been already reported, which finds out a centroid of each class by moving given objects as vectors. We developed new clustering procedure called grey K-means, which is able to handle the number of required clusters such as the hard K-means or the fuzzy c-means. Assume that the number of found clusters by the proposal is between 1 and the number of classified instances, a required threshold value is exist in [0,1]. We defined a value range of the threshold as the interval grey number, and the range is specified automatically until obtaining the required clusters. In addition a new clustering method which analyzes the grey relational matrix closely instead of moving vectors is suggested. Several well-known data sets in the classification problem are applied, and we discuss their performances and the optimal threshold value.

A Kansei Expression Method Based on the Simultaneous GM with the Kansei Map

We propose the K-Model using Multi-Agent Systems (MAS) and dynamic system as a Kansei information processing model. A dynamic system has been realized by Grey Model (GM) in grey system theory. However, the output system in the conventional model was one-dimensional function. And, it was specifically insufficient to show current human emotion. In the emotion analysis system from human voice, the discrimination accuracy remains at around 60%. This paper presents a new proposal for a multi-dimensional Kansei expression method. This method is constructed from the Kansei map and the simultaneous GM. The Kansei map is a map that includes Kansei elements on a two-dimensional plane. This method is introduced into the emotion analysis system from human voice and its evaluation experiment is carried out. Moreover, a data-preprocessing method to establish the GM newly proposed. As a result of the experiment, it is possible that the discrimination accuracy is improved about 20% compared with the conventional method.

Method to consider familiarity in clothing coordination recommender systems

This paper describes a method for clothing coordination recommender systems to consider familiarity by using internal activity factors. We implemented the method in our system for evaluation and conducted experiments using pairs of Kansei words to reflect the impressions of participants. The experimental results show the effectiveness of the method. This method should also be useful for other systems, such as food recommendation systems.

Method to Consider Seasonal Changes in Clothing Coordination Recommender Systems

This paper describes a method enabling clothing coordination recommender systems to consider seasonal changes by using probabilistic state transition models. We implemented the proposed method in our system for evaluation. Experimental results show the method can improve user satisfaction. This method should also be useful for other similar systems, such as food recommendation systems.