The fashion industry is characterised by the need to make demand forecasts in advance and for highly volatile products for which we often have no sales history at the time the forecasts are made.For this reason, it is necessary to propose forecast mechanisms that can cope with the given conditions.Such forecasts can be based on expert predictions for generalized product categories.In this case, the task of machine learning forecasting methods would be to divide the aggregate prediction into forecasts for individual products, in each colour and size.In the paper, we present several approaches to this specific task.We present the use of the naive method, custom nearest neighbour approach, parametric linear mixed model and an ensemble approach.Overall, the best results we obtained for the ensemble method.Our research was based on real data from fashion retail.

SUMMARY: Patient multi-omics datasets are often characterized by a high dimensionality; however, usually only a small fraction of the features is informative, that is change in their value is directly related to the disease outcome or patient survival. In medical sciences, in addition to a robust feature selection procedure, the ability to discover human-readable patterns in the analyzed data is also desirable. To address this need, we created MAINE-Multi-omics Analysis and Exploration. The unique functionality of MAINE is the ability to discover multidimensional dependencies between the selected multi-omics features and event outcome prediction as well as patient survival probability. Learned patterns are visualized in the form of interpretable decision/survival trees and rules. AVAILABILITY AND IMPLEMENTATION: MAINE is freely available at maine.ibemag.pl as an online web application. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rule-based models are often used for data analysis as they combine interpretability with predictive power. We present RuleKit, a versatile tool for rule learning. Based on a sequential covering induction algorithm, it is suitable for classification, regression, and survival problems. The presence of a user-guided induction facilitates verifying hypotheses concerning data dependencies which are expected or of interest. The powerful and flexible experimental environment allows straightforward investigation of different induction schemes. The analysis can be performed in batch mode, through RapidMiner plug-in, or R package. The software is available at GitHub (https://github.com/adaa-polsl/RuleKit) under GNU AGPL-3.0 license.

Automation or even computer assistance of the popular infertility treatment method: ICSI (Intracytoplasmic Sperm Injection) would speed up the whole process and improve the control of the results. This paper introduces a preliminary research for automatic spermatozoon injection into the oocyte cytoplasm. Here, the method for detection a correct orientation of the polar body of the oocyte is presented. Proposed method uses deep learning U-Net architecture for object segmentation. This solution proved to be universal and had no demand for numerous dataset or high-quality Images.

BACKGROUND: Survival analysis is an important element of reasoning from data. Applied in a number of fields, it has become particularly useful in medicine to estimate the survival rate of patients on the basis of their condition, examination results, and undergoing treatment. The recent developments in the next generation sequencing open new opportunities in survival study as they allow vast amount of genome-, transcriptome-, and proteome-related features to be investigated. These include single nucleotide and structural variants, expressions of genes and microRNAs, DNA methylation, and many others. RESULTS: We present LR-Rules, a new algorithm for rule induction from survival data. It works according to the separate-and-conquer heuristics with a use of log-rank test for establishing rule body. Extensive experiments show LR-Rules to generate models of superior accuracy and comprehensibility. The detailed analysis of rules rendered by the presented algorithm on four medical datasets concerning leukemia as well as breast, lung, and thyroid cancers, reveals the ability to discover true relations between attributes and patients' survival rate. Two of the case studies incorporate features obtained with a use of high throughput technologies showing the usability of the algorithm in the analysis of bioinformatics data. CONCLUSIONS: LR-Rules is a viable alternative to existing approaches to survival analysis, particularly when the interpretability of a resulting model is crucial. Presented algorithm may be especially useful when applied on the genomic and proteomic data as it may contribute to the better understanding of the background of diseases and support their treatments.

This paper summarizes AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines which was held between October 5, 2015 and March 4, 2016 at the Knowledge Pit platform.It describes the scope and background of this competition and explains our research objectives which motivated the specific design of the competition rules.The paper also briefly overviews the results of this challenge, showing the way in which those results can help in solving practical problems related to the safety of miners working underground.In particular, our analysis focuses on applications of prediction models in order to facilitate the assessment of seismic hazards, in a situation when the exploration of a given working site has just started and there is very little historical data available.

Underground coal mining is a branch of an industry which safety of operation is very dependent on the natural hazards.A proper seismic event prediction is a significant aspect of building classification models from the real data, which can affect the coal mining safety increase.In this paper four models, built in a well known data mining environments, are presented.The obtained models, depending on a given implementation of popular methods, occurred comparable to the best results from the competition.

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 conflict 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 (8 measures) which had been recognized as effective based on previous conducted research. The study was conducted on approximately 70 benchmark datasets related to classification, regression and survival analysis problems. In the comparisons we analyzed prognostic abilities of the induced rules as well as the complexity of the resulting rule-based data models.

This paper presents the DISESOR integrated decision support system.The system integrates data from different monitoring and dispatching systems and contains such modules as data preparation and cleaning, analytical, prediction and expert system.Architecture of the system is presented in the paper and a special focus is put on the presentation of two issues: data integration and cleaning, and creation of prediction model.The work contains also a case study presenting an example of the system application.

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.