12,725 research outputs found
A new approach for discovering business process models from event logs.
Process mining is the automated acquisition of process models from the event logs of information systems. Although process mining has many useful applications, not all inherent difficulties have been sufficiently solved. A first difficulty is that process mining is often limited to a setting of non-supervised learnings since negative information is often not available. Moreover, state transitions in processes are often dependent on the traversed path, which limits the appropriateness of search techniques based on local information in the event log. Another difficulty is that case data and resource properties that can also influence state transitions are time-varying properties, such that they cannot be considered ascross-sectional.This article investigates the use of first-order, ILP classification learners for process mining and describes techniques for dealing with each of the above mentioned difficulties. To make process mining a supervised learning task, we propose to include negative events in the event log. When event logs contain no negative information, a technique is described to add artificial negative examples to a process log. To capture history-dependent behavior the article proposes to take advantage of the multi-relational nature of ILP classification learners. Multi-relational process mining allows to search for patterns among multiple event rows in the event log, effectively basing its search on global information. To deal with time-varying case data and resource properties, a closed-world version of the Event Calculus has to be added as background knowledge, transforming the event log effectively in a temporal database. First experiments on synthetic event logs show that first-order classification learners are capable of predicting the behavior with high accuracy, even under conditions of noise.Credit; Credit scoring; Models; Model; Applications; Performance; Space; Decision; Yield; Real life; Risk; Evaluation; Rules; Neural networks; Networks; Classification; Research; Business; Processes; Event; Information; Information systems; Systems; Learning; Data; Behavior; Patterns; IT; Event calculus; Knowledge; Database; Noise;
Using attribute construction to improve the predictability of a GP financial forecasting algorithm
Financial forecasting is an important area in computational finance. EDDIE 8 is an established Genetic Programming financial forecasting algorithm, which has successfully been applied to a number of international datasets. The purpose of this paper is to further increase the algorithmās predictive performance, by improving its data space representation. In order to achieve this, we use attribute construction to create new (high-level) attributes from the original (low-level) attributes. To examine the effectiveness of the above method, we test the extended EDDIEās predictive performance across 25 datasets and compare it to the performance of two previous EDDIE algorithms. Results show that the introduction of attribute construction benefits the algorithm, allowing EDDIE to explore the use of new attributes to improve its predictive accuracy
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
As the field of data science continues to grow, there will be an
ever-increasing demand for tools that make machine learning accessible to
non-experts. In this paper, we introduce the concept of tree-based pipeline
optimization for automating one of the most tedious parts of machine
learning---pipeline design. We implement an open source Tree-based Pipeline
Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a
series of simulated and real-world benchmark data sets. In particular, we show
that TPOT can design machine learning pipelines that provide a significant
improvement over a basic machine learning analysis while requiring little to no
input nor prior knowledge from the user. We also address the tendency for TPOT
to design overly complex pipelines by integrating Pareto optimization, which
produces compact pipelines without sacrificing classification accuracy. As
such, this work represents an important step toward fully automating machine
learning pipeline design.Comment: 8 pages, 5 figures, preprint to appear in GECCO 2016, edits not yet
made from reviewer comment
Automating biomedical data science through tree-based pipeline optimization
Over the past decade, data science and machine learning has grown from a
mysterious art form to a staple tool across a variety of fields in academia,
business, and government. In this paper, we introduce the concept of tree-based
pipeline optimization for automating one of the most tedious parts of machine
learning---pipeline design. We implement a Tree-based Pipeline Optimization
Tool (TPOT) and demonstrate its effectiveness on a series of simulated and
real-world genetic data sets. In particular, we show that TPOT can build
machine learning pipelines that achieve competitive classification accuracy and
discover novel pipeline operators---such as synthetic feature
constructors---that significantly improve classification accuracy on these data
sets. We also highlight the current challenges to pipeline optimization, such
as the tendency to produce pipelines that overfit the data, and suggest future
research paths to overcome these challenges. As such, this work represents an
early step toward fully automating machine learning pipeline design.Comment: 16 pages, 5 figures, to appear in EvoBIO 2016 proceeding
kLog: A Language for Logical and Relational Learning with Kernels
We introduce kLog, a novel approach to statistical relational learning.
Unlike standard approaches, kLog does not represent a probability distribution
directly. It is rather a language to perform kernel-based learning on
expressive logical and relational representations. kLog allows users to specify
learning problems declaratively. It builds on simple but powerful concepts:
learning from interpretations, entity/relationship data modeling, logic
programming, and deductive databases. Access by the kernel to the rich
representation is mediated by a technique we call graphicalization: the
relational representation is first transformed into a graph --- in particular,
a grounded entity/relationship diagram. Subsequently, a choice of graph kernel
defines the feature space. kLog supports mixed numerical and symbolic data, as
well as background knowledge in the form of Prolog or Datalog programs as in
inductive logic programming systems. The kLog framework can be applied to
tackle the same range of tasks that has made statistical relational learning so
popular, including classification, regression, multitask learning, and
collective classification. We also report about empirical comparisons, showing
that kLog can be either more accurate, or much faster at the same level of
accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at
http://klog.dinfo.unifi.it along with tutorials
A survey on utilization of data mining approaches for dermatological (skin) diseases prediction
Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data
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