Agnieszka Duraj

Detection of Outlier Information Using Linguistic Summarization

The main goal of automatic summarization of databases is usually to characterize the collection of data in terms of the dominant information involved. In complement to this task, the present paper shows the use of linguistic summarization for the characterization of databases containing textual records through detection of outlier information involved. The method applies a fuzzy measure of similarity between sentences to the summarization result.Certain level of standadization of textual records is assumed.

Outlier detection using linguistically quantified statements

Automatic summary of databases is an important tool in strategic decision‐making. This paper applies the concept of linguistic summaries of databases to outlier detection. The definition of an outlier is closely related to the type of data analyzed and its context. Outlier detection is an important data‐mining technique, which finds applications in a wide range of domains. It can identify defects, remove impurities from the data, and, most of all, it is significant to decision‐making processes. The authors propose a novel definition of an outlier, based on linguistic quantifiers and linguistic summaries. Linguistic quantifiers are employed to express the cardinality of a set of outliers in a natural language. Thus, this paper demonstrates that linguistic summaries proposed by Yager, which provide the ability to model imprecise information, can serve as an effective tool for outlier detection.

Supporting Breast Cancer Diagnosis with Multi-objective Genetic Algorithm for Outlier Detection

Outlier detection in medical data covers a broad spectrum of medical research. In this paper, the authors propose a new approach to outlier detection based on the multi-objective genetic algorithm. In medical data, an outlier may be considered as a deviation which indicates the existence of cancerous cells in the breast. The paper presents the results of tests which were conducted on the set of medical data from the repository. The results of the study indicate that our method can be successfully applied to the medical problem in question.

Multiobjective genetic algorithm for outliers detection

The users of information systems often have to deal with outliers in their data. Such outliers can have negative (i.e. abnormal observations) or positive (i.e. detection of new features) impact on their work. Despite the fact, that several methods of outlier detection already exist, there is still a need to improve them. In this work we propose a method for evolutionary outlier detection. The novelty of our approach is a set of criteria, which are used to decide, whether to treat observation as an outlier or not. Conducted research revealed that our method performs very well on the selected problems.

Effective Outlier Detection Technique with Adaptive Choice of Input Parameters

Detection of outliers can identify defects, remove impurities in the data and what is the most important it supports the decision-making processes. In the paper an outlier detection method based on simultaneous indication of outliers by a group of algorithms is proposed. Three well known algorithms: DBSCAN, CLARANS and COF are considered. They are used simultaneously with iteratively chosen input parameters, which finally guarantee stabilization of the number of detected outliers. The research is based on data retrieved from the Internet service allegro.pl, where comments in online auctions are considered as outliers.

Detection of outlier information by the use of linguistic summaries based on classic and interval-valued fuzzy sets: DURAJ et al.

Automatic summary of databases is an important tool in strategic decision‐making. This paper presents the application of linguistic summaries to outlier detection in databases containing both text and numeric attributes. The proposed method applies Yager’s standard summary based on interval‐valued fuzzy sets. Fuzzy similarity measures are the features which are looked for. Detection of outliers can identify defects, remove impurities from the data, and, most of all, it may provide the basis for decision‐making processes. In this paper, we introduce a definition of an outlier based on linguistic summaries. Feasibility of the method is demonstrated on practical examples.

Efficient Genetic Algorithm for Breast Cancer Diagnosis

In almost all datasets some number of abnormal observations is present. Such outliers may affect the process of data analysis. However several methods of outlier detection already exist, there is still a need to look for a new, more effective ones. In this paper we propose a set of objectives that allows to efficiently identify outliers with the use of multiobjective genetic algorithm. Conducted research shown that such a method can be successfully used with the most common genetic algorithms designed for multiobjective optimization. The results of tests, which were conducted on the set of medical data from the repository, indicate that our method can be successfully applied to the medical problem.

Case-Based Reasoning: The Search for Similar Solutions and Identification of Outliers

The present paper applies the case-based reasoning (CBR) technique to the problem of outlier detection. Although CBR is a widely investigated method with a variety of successful applications in the academic domain, so far, it has not been explored from an outlier detection perspective. This study seeks to address this research gap by defining the outlier case and the underlining specificity of the outlier detection process within the CBR approach. Moreover, the case-based classification (CBC) method is discussed as a task type of CBR. This is followed by the computational illustration of the approach using selected classification methods, that is, linear regression, distance-based classifier, and the Bayes classifier.

Outlier detection in medical data using linguistic summaries

The main purpose of outlier detection algorithms is to find a new feature that is distinct from the other features of the vector in the analyzed data set. This paper concerns outlier detection in medical databases, and the supervised and unsupervised methods used in detection of outliers in medical data are discussed. Moreover, the authors original method for detecting outliers based on linguistic summaries is presented.