Maciej Zięba

Classification Restricted Boltzmann Machine for comprehensible credit scoring model

We propose a comprehensible model for credit risk assessment using a scoring table.We use Restricted Boltzmann Machine to determine scoring points in a scoring table.We deal with the imbalanced data by applying the geometric mean criterion.The quality of the presented method is evaluated on four credit scoring datasets. Credit scoring is the assessment of the risk associated with a consumer (an organization or an individual) that apply for the credit. Therefore, the problem of credit scoring can be stated as a discrimination between those applicants whom the lender is confident will repay credit and those applicants who are considered by the lender as insufficiently reliable. In this work we propose a novel method for constructing comprehensible scoring model by applying Classification Restricted Boltzmann Machines (ClassRBM). In the first step we train the ClassRBM as a standalone classifier that has ability to predict credit status but does not contain interpretable structure. In order to obtain comprehensible model, first we evaluate the relevancy of each of binary features using ClassRBM and further we use these values to create the scoring table (scorecard). Additionally, we deal with the imbalanced data issue by proposing a procedure for determining the cutting point using the geometric mean of specificity and sensitivity. We evaluate our approach by comparing its performance with the results gained by other methods using four datasets from the credit scoring domain.

The Proposal of Service Oriented Data Mining System for Solving Real-Life Classification and Regression Problems

In this work we propose an innovative approach to data mining problem. We propose very flexible data mining system based on service-oriented architecture. Developing applications according to SOA paradigm emerges from the rapid development of the new technology direct known as sustainability science. Each of data mining functionalities is delivered by the execution of the proper Web service. The Web services, described by input and output parameters and the semantic description of its functionalities, are accessible for all applications that are integrated via Enterprise Service Bus.

Services Recommendation in Systems Based on Service Oriented Architecture by Applying Modified ROCK Algorithm

In this work the proposal for services recommendation in online educational systems based on service oriented architecture is introduced. The problem of recommending services responsible for creating student groups are taken into account and as the criterion of the grouping the student learning potential is considered. As a method of grouping modified ROCK algorithm is used during service execution.

Probabilistic combination of classification rules and its application to medical diagnosis

Application of machine learning to medical diagnosis entails facing two major issues, namely, a necessity of learning comprehensible models and a need of coping with imbalanced data phenomenon. The first one corresponds to a problem of implementing interpretable models, e.g., classification rules or decision trees. The second issue represents a situation in which the number of examples from one class (e.g., healthy patients) is significantly higher than the number of examples from the other class (e.g., ill patients). Learning algorithms which are prone to the imbalance data return biased models towards the majority class. In this paper, we propose a probabilistic combination of soft rules, which can be seen as a probabilistic version of the classification rules, by introducing new latent random variable called conjunctive feature. The conjunctive features represent conjunctions of values of attribute variables (features) and we assume that for given conjunctive feature the object and its label (class) become independent random variables. In order to deal with the between class imbalance problem, we present a new estimator which incorporates the knowledge about data imbalanceness into hyperparameters of initial probability of objects with fixed class labels. Additionally, we propose a method for aggregating sufficient statistics needed to estimate probabilities in a graph-based structure to speed up computations. At the end, we carry out two experiments: (1) using benchmark datasets, (2) using medical datasets. The results are discussed and the conclusions are drawn.

Ensemble Classifier for Solving Credit Scoring Problems

The goal of this paper is to propose an ensemble classification method for the credit assignment problem. The idea of the proposed method is based on switching class labels techniques. An application of such techniques allows solving two typical data mining problems: a predicament of imbalanced dataset, and an issue of asymmetric cost matrix. The performance of the proposed solution is evaluated on German Credits dataset.

On-line bayesian context change detection in web service systems

In real-life situations characteristics of Web service systems evolve in time. Therefore, change detection techniques become substantial elements of adaptive procedures for Web service systems management, such as resource allocation and anomaly detection methods. In this paper, we propose an on-line change detector which uses the Bayesian inference. We define two models which describe situations with one change and no change within data. Next we apply Bayesian model comparison for change detection. In order to obtain analytical expressions of model evidences used in the model comparison we provide a coherent framework of change detection which focuses on an approximation of the Bayes factor. The proposed solution, contrary to state-of-the-art methods, works in an on-line fashion and the algorithms computational complexity is proportional to the constant size of the shifting window. Low computational complexity of the change detector enables its application in complex computer networks. At the end of the research paper, the quality of the proposed algorithm is examined using simulated Web service system.

Service-oriented medical system for supporting decisions with missing and imbalanced data.

In this paper, we propose a service-oriented support decision system (SOSDS) for diagnostic problems that is insensitive to the problems of the imbalanced data and missing values of the attributes, which are widely observed in the medical domain. The system is composed of distributed Web services, which implement machine-learning solutions dedicated to constructing the decision models directly from the datasets impaired by the high percentage of missing values of the attributes and imbalanced class distribution. The issue of the imbalanced data is solved by the application of a cost-sensitive support vector machine and the problem of missing values of attributes is handled by proposing the novel ensemble-based approach that splits the incomplete data space into complete subspaces that are further used to construct base learners. We evaluate the quality of the SOSDS components using three ontological datasets.

Analysis of Human Arm Motions Recognition Algorithms for System to Visualize Virtual Arm

This paper presents preliminary studies on the problem of classification of different kinds of human arm motions based on EMG signals. Methods of change detection, classification, features extraction and selection are considered as an important elements of recognition process. Presented algorithms are part of module to visualise human arm movements. The aim of presented work is to develop system to support technical training of tennis player through visualisation of human arm motions and biofeedback.

RBM-SMOTE: Restricted Boltzmann Machines for Synthetic Minority Oversampling Technique

The problem of imbalanced data, i.e., when the class labels are unequally distributed, is encountered in many real-life application, e.g., credit scoring, medical diagnostics. Various approaches aimed at dealing with the imbalanced data have been proposed. One of the most well known data pre-processing method is the Synthetic Minority Oversampling Technique (SMOTE). However, SMOTE may generate examples which are artificial in the sense that they are impossible to be drawn from the true distribution. Therefore, in this paper, we propose to apply Restricted Boltzmann Machine to learn an intermediate representation which transform the SMOTE examples to the ones approximately drawn from the true distribution. At the end of the paper we perform an experiment using credit scoring dataset.

Services Merging, Splitting and Execution in Systems Based on Service Oriented Architecture Paradigm

The aim of the paper is to discuss some selected issues related to services merging, partitioning and execution in systems based on service oriented paradigm. The main feature of such systems is that the required services may be efficiently and flexibly composed of available atomic (elementary) services providing certain and well-defined functionalities. It is rather obvious that the flexibility of such a services delivering system may be limited by the amount and cost of communication necessary to support increasing atomic services granularity. It is assumed that the cost of complex service delivery is composed of exchanged data flows processing and communication costs and the services quality depends on delays introduced by available resources for data flows characterizing services requests followed by specified requirements.