scholarly journals Tuning Expert Systems for Cost-Sensitive Decisions

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Atish P. Sinha ◽  
Huimin Zhao
Keyword(s):  
Data Mining ◽  
Expert System ◽  
Expert Systems ◽  
Domain Knowledge ◽  
Training Data ◽  
Business Decisions ◽  
Misclassification Cost ◽  
Knowledge Based ◽  
The Impact ◽  
And Training

There is currently a growing body of research examining the effects of the fusion of domain knowledge and data mining. This paper examines the impact of such fusion in a novel way by applying validation techniques and training data to enhance the performance of knowledge-based expert systems. We present an algorithm for tuning an expert system to minimize the expected misclassification cost. The algorithm employs data reserved for training data mining models to determine the decision cutoff of the expert system, in terms of the certainty factor of a prediction, for optimal performance. We evaluate the proposed algorithm and find that tuning the expert system results in significantly lower costs. Our approach could be extended to enhance the performance of any intelligent or knowledge system that makes cost-sensitive business decisions.

ACCESSING DATA MINING RULES THROUGH EXPERT SYSTEMS

2002 ◽  
Vol 01 (04) ◽  
pp. 657-672 ◽  
Author(s):  
BASILIS BOUTSINAS
Keyword(s):  
Data Mining ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Discovery ◽  
Domain Knowledge ◽  
Research Area ◽  
General Purpose ◽  
Related Data ◽  
Different Types

Data mining is an emerging research area that develops techniques for knowledge discovery in huge volumes of data. Usually, data mining rules can be used either to classify data into predefined classes, or to partition a set of patterns into disjoint and homogeneous clusters, or to reveal frequent dependencies among data. The discovery of data mining rules would not be very useful unless there are mechanisms to help analysts access them in a meaningful way. Actually, documenting and reporting the extracted knowledge is of considerable importance for the successful application of data mining in practice. In this paper, we propose a methodology for accessing data mining rules, which is based on using an expert system. We present how the different types of data mining rules can be transformed into the domain knowledge of any general-purpose expert system. Then, we present how certain attribute values given by the user as facts and/or goals can determine, through a forward and/or backward chaining, the related data mining rules. In this paper, we also present a case study that demonstrates the applicability of the proposed methodology.

scholarly journals Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics

2021 ◽  
Vol 7 (3) ◽  
pp. 59
Author(s):  
Yohanna Rodriguez-Ortega ◽  
Dora M. Ballesteros ◽  
Diego Renza
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Training Data ◽  
Video Editing ◽  
Forgery Detection ◽  
Copy Move Forgery Detection ◽  
The Impact ◽  
And Training ◽  
Selection Of ◽  
Traditional Image

With the exponential growth of high-quality fake images in social networks and media, it is necessary to develop recognition algorithms for this type of content. One of the most common types of image and video editing consists of duplicating areas of the image, known as the copy-move technique. Traditional image processing approaches manually look for patterns related to the duplicated content, limiting their use in mass data classification. In contrast, approaches based on deep learning have shown better performance and promising results, but they present generalization problems with a high dependence on training data and the need for appropriate selection of hyperparameters. To overcome this, we propose two approaches that use deep learning, a model by a custom architecture and a model by transfer learning. In each case, the impact of the depth of the network is analyzed in terms of precision (P), recall (R) and F1 score. Additionally, the problem of generalization is addressed with images from eight different open access datasets. Finally, the models are compared in terms of evaluation metrics, and training and inference times. The model by transfer learning of VGG-16 achieves metrics about 10% higher than the model by a custom architecture, however, it requires approximately twice as much inference time as the latter.

Integrating Rule-Based Systems and Data Analytics Tools Using Open Standard PMML

2015 ◽  
Author(s):  
Yunpeng Li ◽  
Utpal Roy ◽  
Y. Tina Lee ◽  
Sudarsan Rachuri
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Analytics ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Data Driven ◽  
Sensor Data ◽  
Formal Representation ◽  
Rule Based ◽  
Open Standard

Rule-based expert systems such as CLIPS (C Language Integrated Production System) are 1) based on inductive (if-then) rules to elicit domain knowledge and 2) designed to reason new knowledge based on existing knowledge and given inputs. Recently, data mining techniques have been advocated for discovering knowledge from massive historical or real-time sensor data. Combining top-down expert-driven rule models with bottom-up data-driven prediction models facilitates enrichment and improvement of the predefined knowledge in an expert system with data-driven insights. However, combining is possible only if there is a common and formal representation of these models so that they are capable of being exchanged, reused, and orchestrated among different authoring tools. This paper investigates the open standard PMML (Predictive Model Mockup Language) in integrating rule-based expert systems with data analytics tools, so that a decision maker would have access to powerful tools in dealing with both reasoning-intensive tasks and data-intensive tasks. We present a process planning use case in the manufacturing domain, which is originally implemented as a CLIPS-based expert system. Different paradigms in interpreting expert system facts and rules as PMML models (and vice versa), as well as challenges in representing and composing these models, have been explored. They will be discussed in detail.

The Evolving Role of Task-Group Leaders, A Field Study

1988 ◽  
Vol 32 (13) ◽  
pp. 760-764
Author(s):  
Robert F. Randolph
Keyword(s):  
Group Performance ◽  
Training Data ◽  
Underground Coal Mining ◽  
Group Leaders ◽  
Underground Coal Mines ◽  
And Performance ◽  
Task Oriented ◽  
The Impact ◽  
And Training

Leaders of task-oriented production groups play an important role in their group's functioning and performance. That role also evolves as groups mature and learn to work together more smoothly. The present study uses a functional analysis of the evolving role of supervisors of underground coal mining crews to evaluate the impact of supervisors' characteristics and behaviors on their crews' efficiency and safety, and makes recommendations for improving supervisory selection and training. Data were gathered from a sample of 138 supervisors at 13 underground coal mines. Detailed structured observations of the supervisors indicated that most of their time was spent attending to hardware and paperwork, while comparatively little time was spent on person to person “leadership”. The findings point out that while group needs changed over time, the supervisors' behaviors typically did not keep pace and probably restricted group performance.

scholarly journals Mapping to Declarative Knowledge for Word Problem Solving

2018 ◽  
Vol 6 ◽  
pp. 159-172
Author(s):  
Subhro Roy ◽  
Dan Roth
Keyword(s):  
Problem Solving ◽  
Word Problem ◽  
Latent Variable ◽  
Domain Knowledge ◽  
Declarative Knowledge ◽  
Training Data ◽  
Mathematical Concepts ◽  
Word Problem Solving ◽  
Knowledge Based ◽  
Financial News

Math word problems form a natural abstraction to a range of quantitative reasoning problems, such as understanding financial news, sports results, and casualties of war. Solving such problems requires the understanding of several mathematical concepts such as dimensional analysis, subset relationships, etc. In this paper, we develop declarative rules which govern the translation of natural language description of these concepts to math expressions. We then present a framework for incorporating such declarative knowledge into word problem solving. Our method learns to map arithmetic word problem text to math expressions, by learning to select the relevant declarative knowledge for each operation of the solution expression. This provides a way to handle multiple concepts in the same problem while, at the same time, supporting interpretability of the answer expression. Our method models the mapping to declarative knowledge as a latent variable, thus removing the need for expensive annotations. Experimental evaluation suggests that our domain knowledge based solver outperforms all other systems, and that it generalizes better in the realistic case where the training data it is exposed to is biased in a different way than the test data.

Research to Safety Evaluation Expert System of Hydraulic Engineering Metal Structures

2011 ◽  
Vol 48-49 ◽  
pp. 994-1001 ◽  
Author(s):  
Guang Ming Yang
Keyword(s):  
Expert System ◽  
Relational Database ◽  
Domain Knowledge ◽  
Safety Evaluation ◽  
Database System ◽  
Base System ◽  
Metal Structures ◽  
Knowledge Based ◽  
Knowledge Base System ◽  
Method Base

Combining the successful applications in AI, in this paper, an expert system is studied and designed for evaluating the safety of hydraulic metal structures, whose goal is compute the reliability of hydraulic metal structures. Applying the techniques of AI, a framework is made up for evaluating the safety of hydraulic metal structures. The framework of knowledge base system is designed and presented with the domain knowledge. Based on the theory of relational database, the conceptual and logical views of database system are designed and analysed. Additionally, method base system is designed. A practical example is given to illustrate the process of using this system. This system has features of practical and advanced and expand.

A PROPOSED ARCHITECTURE FOR SELF-ADAPTIVE EXPERT SYSTEMS

2009 ◽  
Vol 19 (02) ◽  
pp. 213-248 ◽  
Author(s):  
TSUNG-TENG CHEN ◽  
CHENG-SEEN HO
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Domain Knowledge ◽  
Semantic Networks ◽  
Inference Process ◽  
Operating Environment ◽  
Traditional System ◽  
User Input ◽  
Adaptive Capability ◽  
Self Adaptive

The pre-built knowledge of traditional expert systems is only capable of limited responses to changes in the operating environment. If the data input is imperfect, a traditional system may fail to reach any rational conclusions. In this paper, we introduce the concept of self-adaptability to the inference process of an expert system, and propose a model that is capable of handling unexpected user input effectively and efficiently. Such a system can formulate operational knowledge on the move for inference. With this self-adaptive capability, an expert system can reach useful conclusions, even when the input data is insufficient. The architecture of the proposed system encodes domain knowledge with semantic networks. It also defines four types of adaptation, namely, condition knowledge adaptation, operational knowledge adaptation, conclusion knowledge adaptation, and presentation adaptation, and focuses on how the first three contribute to the adaptive capability of the system. In addition, to enable a self-adaptive expert system to effectively produce better conclusions, two entropy-based measuring mechanisms are proposed: one minimizes the information loss during knowledge adaptation, while the other selects the best attribute relation during the generation of operational knowledge. We have proved that a self-adaptive expert system based on this architecture can always reach a regular conclusion or an abstract conclusion, which is a more meaningful conclusion by automatically modifying its operational knowledge in response to user feedback during the inference process, even in unexpected situations.

scholarly journals An Expert System for Local Flood Response Coordination and Training

2017 ◽  
Vol 6 (2) ◽  
pp. 1
Author(s):  
Xiaoyin Zhang ◽  
Gary Moynihan ◽  
Andrew Ernest ◽  
Joseph Gutenson
Keyword(s):  
Expert System ◽  
Local Governments ◽  
Flood Management ◽  
Emergency Managers ◽  
Structure Report ◽  
Coordination System ◽  
Flood Response ◽  
Core Capabilities ◽  
The Impact ◽  
And Training

Flood response is an essential component of flood management to rescue people, reduce property loss, and limit the impact to the environment. Effective flood response depends on a sound coordination structure with unified responsibilities, smooth communications, and scalable response plans. An efficient coordination system, including command and management structures, is built on a thorough understanding of the responsibilities and actions of each role for delivering the response core capabilities. Collecting, sharing, using, and handling the knowledge require great efforts in knowledge management. To further enhance such efforts, an expert system for local flood response coordination and training (LFRS) was developed and introduced in this paper. LFRS can help emergency managers construct scalable, flexible, and adaptable coordination structures and support educating flood response entities, such as individuals, communities, nongovernmental organizations, private sector entities, and local governments. The output of the prototype expert system contains two CSV formatted reports as well as prompt screens. The operational structure report hierarchically depicts the crisscross linkages among all responders, their primary functions, and contact information. Another report summarizes the responsibilities and actions of a certain role of flood responders from commanders to individuals.

scholarly journals Efficient and effective OCR engine training

2019 ◽  
Vol 23 (1) ◽  
pp. 73-88
Author(s):  
Christian Clausner ◽  
Apostolos Antonacopoulos ◽  
Stefan Pletschacher
Keyword(s):  
Evaluation System ◽  
Training Data ◽  
Text Recognition ◽  
Training Approach ◽  
Efficiency And Effectiveness ◽  
Analysis System ◽  
The Impact ◽  
Data Requirements ◽  
And Training

Abstract We present an efficient and effective approach to train OCR engines using the Aletheia document analysis system. All components required for training are seamlessly integrated into Aletheia: training data preparation, the OCR engine’s training processes themselves, text recognition, and quantitative evaluation of the trained engine. Such a comprehensive training and evaluation system, guided through a GUI, allows for iterative incremental training to achieve best results. The widely used Tesseract OCR engine is used as a case study to demonstrate the efficiency and effectiveness of the proposed approach. Experimental results are presented validating the training approach with two different historical datasets, representative of recent significant digitisation projects. The impact of different training strategies and training data requirements is presented in detail.

USING EXPERT SYSTEMS FOR IMAGE UNDERSTANDING

1987 ◽  
Vol 01 (02) ◽  
pp. 237-250 ◽  
Author(s):  
P. SUETENS ◽  
A. OOSTERLINCK
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Expert Systems ◽  
Review Paper ◽  
Image Understanding ◽  
Interested Reader ◽  
Knowledge Based ◽  
Survey Papers ◽  
Application Fields

Expert systems and image understanding have traditionally been considered as two separate application fields of artificial intelligence (AI). In this paper it is shown, however, that the idea of building an expert system for image understanding may be fruitful. Although this paper may serve as a framework for situating existing works on knowledge-based vision, it is not a review paper. The interested reader will therefore be referred to some recommended survey papers in the literature.

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