Muneyuki Unehara

Rough set model based on Parameterized Probabilistic similarity relation in incomplete decision tables

This paper discusses some extension of Rough set approach in Incomplete decision tables to deal with a problem of tolerance relation. Those approaches have been widely used to discover knowledge in incomplete information system. However, they also have their own limitation. In order to get more information from the relationship among objects, we propose a model called Parameterized Probabilistic Rough Set for incomplete decision tables. First we defined the probability of similarity between two objects if there is unavailable information. Then this probability is combined with a comparison based on available attribute values to derive a new relation.

Rough Sets and Rule Induction in Imperfect Information Systems

The original rough set theory deals with precise and complete data, while real applications frequently contain imperfect information. A typical imperfect data studied in rough set research is the missing values. Though there are many ideas proposed to solve the issue in the literature, the paper adopts a probabilistic approach, because it can incorporate other types of imperfect data including imprecise and uncertain values in a single approach. The paper first discusses probabilities of attribute values assuming different type of attributes in real applications, and proposes a generalized method of probability of matching. This probability is then used to define valued tolerance/similarity relations and to develop new rough set models based on the valued tolerance/similarity relations. An algorithm for deriving decision rules based on the rough set models is also studied and proposed.

Evaluation of hybrid optimization with EMO and IEC for architectural floor planning

We investigate the combinatorial effect of evolutionary multi-objective optimization (EMO) with interactive evolutionary computation (IEC). The purposes and combination ways of several presented EMO and IEC researches are different. We evaluated seven combination ways of four EMO objectives given by fitness functions and one IEC objective given by a pseudo-IEC user outputting stable evaluation regardless repeated experiments in our previous experiments. In this paper, we extend experimental conditions to 39 and evaluate them: 3 pseudo-users X 13 combination ways of 4 + 1 objectives. We also consider features of this system.

Protein named entity classification with probabilistic features derived from GENIA corpus and MEDLINE

Biomédical named entity recognition (BNER) is one of the most essential and initial tasks (discovering relations between biomédical entities, identifying molecular pathways, etc.) of biomédical information retrieval. Although named entity recognition performed well in ordinary text, it still remains challenging in molecular biology domain because of the complex nature of biomédical nomenclature, different kinds of spelling forms and many more reasons. Even though biomédical entities in biological text are found successfully, classifying them into relevant biomédical classes such as genes, proteins, diseases, drug names, etc. is still another challenge and an open question. This paper presents a new method to classify biomédical named entities into protein and non-protein classes. Our approach employs Random Forest, a machine learning algorithm, with a new combination of features. They are orthographic, keyword and morphological, as well as a probabilistic feature called Proteinhood and a Protein-Score feature based on the Medline abstracts cited on the Pubmed, which are the main contributions in the paper. A series of experiments is conducted to compare the proposed approach with other state of the art approaches. Our protein named entity classifier shows significant performance in the experiments on GENIA corpus achieving the highest values of precision 93.8%, recall 83.8% and F-measure 88.5% for protein named entity identification. In this study we showed the effect of new Proteinhood and Protein-Score features as well as adjusting parameters of Random Forest algorithm.

Protein Named Entity Identification Based on Probabilistic Features Derived from GENIA Corpus and Medical Text on the Web

Protein named entity identification is one of the most essential and fundamental predecessor for extracting information about protein-protein interactions from biomedical literature. In this paper, we explore the use of abstracts of biomedical literature in MEDLINE for protein name identification and present the results of the conducted experiments. We present a robust and e ective approach to classify biomedical named entities into protein and non-protein classes, based on a rich set of features: orthographic, keyword, morphological and newly introduced Protein-Score features. Our procedure shows significant performance in the experiments on GENIA corpus using Random Forest, achieving the highest values of precision 92.7%, recall 91.7%, and F-measure 92.2% for protein identification, while reducing the training and testing time significantly.

On Probability of Matching in Probability Based Rough Set Definitions

The original rough set theory deals with precise and complete data, while real applications frequently contain imperfect information. A typical imperfect data studied in rough set research is the missing values. Though there are many ideas proposed to solve the issue in the literature, the paper adopts a probabilistic approach, because it can incorporate other types of imperfect data including imprecise and uncertain values in a single approach. The paper first discusses probabilities of attribute values assuming different type of attributes in real applications, and proposes a generalized method of probability of matching. It also discusses the case of continuous data as well as discrete one. The proposed probability of matching could be used for defining valued tolerance/similarity relations in rough set approaches.

Parallel Computation of Rough Set Approximations in Information Systems with Missing Decision Data

The paper discusses the use of parallel computation to obtain rough set approximations from large-scale information systems where missing data exist in both condition and decision attributes. To date, many studies have focused on missing condition data, but very few have accounted for missing decision data, especially in enlarging datasets. One of the approaches for dealing with missing data in condition attributes is named twofold rough approximations. The paper aims to extend the approach to deal with missing data in the decision attribute. In addition, computing twofold rough approximations is very intensive, thus the approach is not suitable when input datasets are large. We propose parallel algorithms to compute twofold rough approximations in large-scale datasets. Our method is based on MapReduce, a distributed programming model for processing large-scale data. We introduce the original sequential algorithm first and then the parallel version is introduced. Comparison between the two approaches through experiments shows that our proposed parallel algorithms are suitable for and perform efficiently on large-scale datasets that have missing data in condition and decision attributes.

Interactive decoration design support system by fitness evaluation based on design knowledge and subjective evaluation

This paper proposes interactive decoration design support system introducing interactive evolutionary computation. Evaluation problem existing human can be evaluated synthesizing multi objectives. The proposed system made it importance dividing the human evaluation into two part, which is including regular level of design quality and users subjective evaluation by affective or Kansei image, and assumed to acquire good results by executing in serial order in a certain evaluation phase. From the experimental results, effectiveness of proposed methodology involving evolution by using fitness evaluation of decoration designs for quality and evolution by user evaluation are confirmed.

Semi-supervised based rough set to handle missing decision data

We have developed a rough set model for analyzing an information system in which some conditions as well as decision values, are missing. Current studies have focused mainly on the missing of condition data but seem to ignore the missing of decision data. The common approach is to remove objects with no decision values because such objects are apparently considered fruitless from the decision-making standpoint. However, this deletion may lead to the risk of information loss. We observe that such a situation is somewhat similar to the semi-supervised situation in the sense that some objects are characterized by complete decision data while some are not. Considering both kinds of objects from a probabilistic view, we predict potential candidates for missing values by comparing measurements of two factors, local decision belief and universal decision belief, with a parameter threshold α. These possible decision candidates help to form a relative dissimilarity relation, which measures the unlikeness of pairs of objects rather than their likeness. Contrasting with the other approaches, rough set definitions based on this relation do not approximate the target set but its complement instead. The knowledge acquisition induced by the common approach and the proposed approach is compared, and the result shows that the latter can overcome some limitations of the former. This approach is new and flexible to deal with missing decision information.

Simulating associations and interactions among multiple pieces of brand image using Fuzzy Bidirectional Associative Memory

The paper discusses an idea of representing brand image on a computer and simulating associations and interactions among multiple pieces of brand image. Brand image is represented using a fuzzy set based on the theory of brand personality, which is a theory to represent brand image indirectly by a set of human characteristics associated with a brand. An convenient feature of the representation is generality that image of any kind of brands could be defined on the same universal set. The interactions among multiple pieces of image are simulated using the framework of conceptual fuzzy set which is realized as combination of two fuzzy bidirectional associative memories.