RSCTC’2010 Discovery Challenge: Mining DNA Microarray Data for Medical Diagnosis and Treatment

Wojnarski, Marcin; Janusz, Andrzej; Nguyen, Hung Son; Bazan, Jan G.; Luo, Cheng; Chen, Ze; Hu, Feng; Wang, Guoyin; Guan, Lihe; Luo, Haipeng; Gao, Juan; Shen, Yuanxia; Nikulin, V.; Huang, Tian-Hsiang; McLachlan, Geoffrey J.; Bošnjak, Matko; Gamberger, Dragan

doi:10.1007/978-3-642-13529-3_3

Cited by 26 publications

(13 citation statements)

References 12 publications

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“…In the case of 2-class problems, balanced accuracy is equivalent to Area Under the ROC Curve (AUC) criterion [38]. One can meet with the interpretation that balanced accuracy calculated for a number of classes is a generalization of AUC for multi-class problems [44].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Rule Quality Measures Settings in a Sequential Covering Rule Induction Algorithm - an Empirical Approach

Michalak

Sikora

Wróbel

2015

Annals of Computer Science and Information Systems

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Abstract-The paper presents the results of research related to the efficiency of the so called rule quality measures which are used to evaluate the quality of rules at each stage of the rule induction. The stages of rule growing and pruning were considered along with the issue of conflicts resolution which may occur during the classification. The work is the continuation of research on the efficiency of quality measures employed in sequential covering rule induction algorithm. In this paper we analyse only these quality measures (9 measures) which had been recognised as effective based on previously conducted research.

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Section: Experiments and Resultsmentioning

confidence: 99%

Rule Quality Measures Settings in a Sequential Covering Rule Induction Algorithm - an Empirical Approach

Michalak

Sikora

Wróbel

2015

Annals of Computer Science and Information Systems

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“…The utilized feature selection methods were based on correlation test [135], t-test [88,136] and the relief algorithm [138], respectively. Table 14 shows the results of this comparison for six data tables from the basic track of RSCTC'2010 Discovery Challenge [37]. The results are also visualized in Figure 17.…”

Section: Evaluation Of the Dynamic Rule-based Similarity Model On Micmentioning

confidence: 95%

“…There are also several other research directions of the author that had a significant influence on the design of the proposed similarity learning models. Among them, the most important considered the problem of feature selection and learning with ensembles of single and multi-label classifiers [30][31][32][33][34][35][36][37]. Moreover, the research on unsupervised version of Rule-Based Similarity was largely influenced by the author's previous work on the semantic information retrieval and Explicit Semantic Analysis, which was conducted within the SYNAT project [38][39][40].…”

Section: Main Contributionsmentioning

confidence: 99%

Algorithms for Similarity Relation Learning from High Dimensional Data

Janusz

2014

Transactions on Rough Sets XVII

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Abstract. The notion of similarity plays an important role in machine learning and artificial intelligence. It is widely used in tasks related to a supervised classification, clustering, an outlier detection and planning. Moreover, in domains such as information retrieval or case-based reasoning, the concept of similarity is essential as it is used at every phase of the reasoning cycle. The similarity itself, however, is a very complex concept that slips out from formal definitions. A similarity of two objects can be different depending on a considered context. In many practical situations it is difficult even to evaluate the quality of similarity assessments without considering the task for which they were performed. Due to this fact the similarity should be learnt from data, specifically for the task at hand. This paper presents a research on the problem of similarity learning, which is a part of author's PHD dissertation. It describes a similarity model, called Rule-Based Similarity, and shows algorithms for constructing this model from available data. The model utilizes notions from the rough set theory to derive a similarity function that allows to approximate the similarity relation in a given context. It is largely inspired by the idea of Tversky's feature contrast model and it has several analogical properties. In the paper, those theoretical properties are described and discussed. Moreover, the paper presents results of experiments on real-life data sets, in which a quality of the proposed model is thoroughly evaluated and compared with the state-of-the-art algorithms.

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“…This particular criterion had been already used in several others data mining competitions, e.g. [18]. It is insensitive to skewed distribution of decisions and thus promotes classifiers which are able to robustly identify labels of cases from minority classes.…”

Section: B Evaluation Proceduresmentioning

confidence: 99%

Tagging Firefighter Activities at the Emergency Scene: Summary of AAIA’15 Data Mining Competition at Knowledge Pit

Meina¹,

Janusz²,

Rykaczewski³

et al. 2015

Annals of Computer Science and Information Systems

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Abstract-In this paper, we summarize AAIA'15 data mining competition: Tagging Firefighter Activities at a Fire Scene, which was held between March 9 and July 6, 2015. We describe the scope and background of the competition. We also reveal details regarding the data set used in the competition, which was collected and tagged specifically for the purpose of this data challenge. We explain the data acquisition process which involved using a body sensor network system consisting of several inertial measurement units and a physiological data sensor. Finally, we briefly discuss submitted results with respect to their possible real-life application in our decision support system.

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RSCTC’2010 Discovery Challenge: Mining DNA Microarray Data for Medical Diagnosis and Treatment

Cited by 26 publications

References 12 publications

Rule Quality Measures Settings in a Sequential Covering Rule Induction Algorithm - an Empirical Approach

Rule Quality Measures Settings in a Sequential Covering Rule Induction Algorithm - an Empirical Approach

Algorithms for Similarity Relation Learning from High Dimensional Data

Tagging Firefighter Activities at the Emergency Scene: Summary of AAIA’15 Data Mining Competition at Knowledge Pit

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