Sebastian Stawicki

TunedIT.org: system for automated evaluation of algorithms in repeatable experiments

In this paper we present TUNEDIT system which facilitates evaluation and comparison of machine-learning algorithms. TUNEDIT is composed of three complementary and interconnected components: TunedTester, Repository and Knowledge Base. TunedTester is a stand-alone Java application that runs automated tests (experiments) of algorithms. Repository is a database of algorithms, datasets and evaluation procedures used by TunedTester for setting up a test. Knowledge Base is a database of test results. Repository and Knowledge Base are accessible through TUNEDIT website. TUNEDIT is open and free for use by any researcher. Every registered user can upload new resources to Repository, run experiments with TunedTester, send results to Knowledge Base and browse all collected results, generated either by himself or by others. As a special functionality, built upon the framework of automated tests, TUNEDIT provides a platform for organization of on-line interactive competitions for machine-learning problems. This functionality may be used, for instance, by teachers to launch contests for their students instead of traditional assignment tasks; or by organizers of machine-learning and data-mining conferences to launch competitions for the scientific community, in association with the conference.

JRS’2012 Data Mining Competition: Topical Classification of Biomedical Research Papers

We summarize the JRS’2012 Data Mining Competition on “Topical Classification of Biomedical Research Papers”, held between January 2, 2012 and March 30, 2012 as an interactive on-line contest hosted on the TunedIT platform ( http://tunedit.org ). We present the scope and background of the challenge task, the evaluation procedure, the progress, and the results. We also present a scalable method for the contest data generation from biomedical research papers.

Decision bireducts and decision reducts – a comparison

Abstract In this paper we revise the notion of decision bireducts. We show new interpretations and we prove several important and practically useful facts regarding this notion. We also explain the way in which some of the well-known algorithms for computation of decision reducts can be modified for the purpose of computing decision bireducts. For the sake of completeness of our study we extend our investigations to relations between decision bireducts and so-called approximate decision reducts. We compare different formulations of those two approaches and draw analogies between them. We also report new results related to NP-hardness of searching for optimal decision bireducts and approximate decision reducts from data. Finally, we present new results of empirical tests which demonstrate usefulness of decision bireducts in a construction of efficient, yet simple ensembles of classification models.

Recent Advances in Decision Bireducts: Complexity, Heuristics and Streams

We continue our research on decision bireducts. For a decision system

Mining Data from Coal Mines: IJCRS’15 Data Challenge

\mathbb{A}

Rough Set Tools for Practical Data Exploration

= U,A i¾? {d}, a decision bireduct is a pair B,X, where B⊆A is a subset of attributes discerning all pairs of objects in X⊆U with different values on the decision attribute d, and where B and X cannot be, respectively, reduced and extended. We report some new results related to NP-hardness of extraction of optimal decision bireducts, heuristics aimed at searching for sub-optimal decision bireducts, and applications of decision bireducts to stream data mining.

Window-based feature extraction framework for multi-sensor data: A posture recognition case study

We summarize the data mining competition associated with IJCRS’15 conference – IJCRS’15 Data Challenge: Mining Data from Coal Mines, organized at Knowledge Pit web platform. The topic of this competition was related to the problem of active safety monitoring in underground corridors. In particular, the task was to design an efficient method of predicting dangerous concentrations of methane in longwalls of a Polish coal mine. We describe the scope and motivation for the competition. We also report the course of the contest and briefly discuss a few of the most interesting solutions submitted by participants. Finally, we reveal our plans for the future research within this important subject.

Window-Based Feature Engineering for Prediction of Methane Threats in Coal Mines

We discuss a rough-set-based approach to the data mining process. We present a brief overview of rough-set-based data exploration and software systems for this purpose that were developed over the years. Then, we introduce the RapidRoughSets extension for the RapidMiner integrated software platform for machine learning and data mining, along with RoughSets package for R System – the leading software environment for statistical computing. We conclude with discussion of the road ahead for rough set software systems.

A Metadata Diagnostic Framework for a New Approximate Query Engine Working with Granulated Data Summaries

The article introduces a novel mechanism for automatic extraction of features from streams of numerical data. It was originally designed for the purpose of processing multiple streams of readings generated by sensors in coal mines. The original research was conducted on methane concentration analysis in the DISESOR project. The article demonstrates an application of the elaborated mechanism for the case of tagging short series of readings from sensors that monitor activities and movements of firefighters during the action with labels corresponding to firefighter activities. The purpose of the experiment was to assess how the automatic feature extraction and construction of classifiers (without parameters tuning and without the use of classifier ensembles) can cope with the competitions task in comparison to other participants.

SONCA: Scalable Semantic Processing of Rapidly Growing Document Stores

We present our results of experiments concerning the methane threats prediction in coal mines obtained during IJCRS’15 Data Challenge. The data mining competition task poses the problem of active monitoring and early threats detection which is essential to prevent spontaneous gas explosions. This issue is very important for the safety of people and equipment as well as minimization of production losses. The discussed research was conducted also to verify the effectiveness of the feature engineering framework developed in the DISESOR project. The utilized framework is based on a sliding window approach and is designed to handle numerous streams of sensor readings.