Danuta Rutkowska

An Orthogonal Series Estimate of Time-Varying Regression

SummaryLet (X1,Y1), (X2,Y2),... be independent pairs of random variables according to the modelYn=tn(Xn)R(Xn)+Zn,n=1,2,..., wheretn andR are unknown functions.Zns are i.i.d. random variables with zero mean and finite variance. The marginal density ofXn is independent ofn. In the paper nonparametric estimates of a nonstationary regression function E{Yn|Xn=x}=tn(x)R(x) are proposed and their asymptotic properties are investigated.

Medical Diagnosis with Type-2 Fuzzy Decision Trees

In this paper, we propose type-2 fuzzy decision trees in application to medical diagnosis. This means that attribute values employed in the tree structures may be characterized by type-2 fuzzy sets. Three medical benchmark data sets, available on the Internet, have been used to illustrate results of diagnosis obtained by this method.

Type-2 Fuzzy Decision Trees

This paper presents type-2 fuzzy decision trees (T2FDTs) that employ type-2 fuzzy sets as values of attributes. A modified fuzzy double clustering algorithm is proposed as a method for generating type-2 fuzzy sets. This method allows to create T2FDTs that are easy to interpret and understand. To illustrate performace of the proposed T2FDTs and in order to compare them with results obtained for type-1 fuzzy decision trees (T1FDTs), two benchmark data sets, available on the internet, have been used.

A new version of the Fuzzy-ID3 algorithm

In this paper, a new version of the Fuzzy-ID3 algorithm is presented. The new algorithm allows to construct decision trees with smaller number of nodes. This is because of the modification that many different attributes and their values can be assigned to single leaves of the tree. The performance of the algorithm was checked on three typical benchmarks data available on the Internet.

Ant Focused Crawling Algorithm

This paper presents a new algorithm for hypertext graph crawling. Using an ant as an agent in a hypertext graph significantly limits amount of irrelevant hypertext documents which must be downloaded in order to download a given number of relevant documents. Moreover, during all time of the crawling, artificial ants do not need a queue to central control crawling process. The proposed algorithm, called the Focused Ant Crawling Algorithm, for hypertext graph crawling, is better than the Shark-Search crawling algorithm and the algorithm with best-first search strategy utilizing a queue for the central control of the crawling process.

Face Classification Based on Linguistic Description of Facial Features

This paper presents an artificial intelligence approach towards classification of persons based on verbal descriptions of their facial features. Frame knowledge representation, fuzzy sets, fuzzy IF-THEN rules, and fuzzy granulation are employed. Features of face elements (nose, eyes, etc.) are extracted by use of existing detection techniques, such as measurements of horizontal and vertical sizes. Linguistic variables that correspond to fuzzy sets, representing selected facial features, are applied in the frames and fuzzy rules. Linguistic values defined by the fuzzy sets conform the terminology applied by law enforcement to create an eyewitness verbal description. Classification results are illustrated in three cases of the system’s input: facial composites (sketches) created by an artist, images (digital pictures) from a face database, and verbal descriptions.

Neuro-Fuzzy Architectures with Various Implication Operators

Neuro fuzzy architectures of fuzzy systems based on various fuzzy implications are considered and their performance is analysed in the paper.

Fuzzy Granulation Approach to Color Digital Picture Recognition

This paper presents a new approach to color digital picture recognition, especially classification of pictures described by linguistic terms. Fuzzy granulation is proposed to express a picture as a composition of fuzzy granules that carry information about color, location, and size, each of these attributes represented by fuzzy sets characterized by membership functions. With regard to the color, the CIE chromaticity triangle is applied, with the concept of fuzzy color areas. The classification result is obtained based on fuzzy IF-THEN rules and fuzzy logic inference employed in a fuzzy system.

Information Granules in Application to Image Recognition

The paper concerns specific problems of color digital image recognition by use of the concept of fuzzy and rough granulation. This idea employs information granules that contain pieces of knowledge about digital pictures such as color, location, size, and shape of an object to be recognized. The object information granule (OIG) is introduced, and the Granular Pattern Recognition System (GPRS) proposed, in order to solve different tasks formulated with regard to the information granules.

Color Digital Picture Recognition Based on Fuzzy Granulation Approach

The paper concerns specific problems of color digital picture recognition by use of the concept of fuzzy granulation, and in addition rough information granulation. This idea employs information granules that contain pieces of knowledge about digital pictures such as location of objects as well as their size and color. Each of those attributes is described by means of linguistic values of fuzzy sets, and the shape attribute is also considered with regard to the rough sets. The picture recognition approach is focused on retrieving a picture (or pictures) from a large collection of color digital pictures (images) - based on the linguistic description of a specific object included in the picture to be recognized.