Optimization and Interpretation of Rule-Based Classifiers

Duch, Włodzisław; Jankowski, Norbert; Grąbczewski, Krzysztof; Adamczak, Rafał

doi:10.1007/978-3-7908-1846-8_1

Cited by 6 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Coronary artery disease data sets from the UCI database repository attract many researchers to build decision systems (Detrano et al, 1989;Duch et al, 2000;Gennari et al, 1989;Jankowski & Kadirkamanathan, 1997;Liping & Lingyun, 2005;Pedreira et al, 2005;Polat et al, 2006;Polat et al, 2007). The UCI-CAD data sets contain 920 patients which are collected from Cleveland Clinic Foundation U.S. (303 patients); Hungarian Institute of Cardiology, Budapest, Hungary (294 patients); Veterans Administration Medical Center, Long Beach, California, U.S. (123 patients) and University Hospital, Zurich, Switzerland (200 patients).…”

Section: Methods and Materials Datamentioning

confidence: 99%

Fuzzy Decision Support System for Coronary Artery Disease Diagnosis Based on Rough Set Theory

Setiawan

2014

International Journal of Rough Sets and Data Analysis

View full text Add to dashboard Cite

The objective of this research is to develop an evidence based fuzzy decision support system for the diagnosis of coronary artery disease. The development of decision support system is implemented based on three processing stages: rule generation, rule selection and rule fuzzification. Rough Set Theory (RST) is used to generate the classification rules from training data set. The training data are obtained from University California Irvine (UCI) data repository. Rule selection is conducted by transforming the rules into a decision table based on unseen data set. Furthermore, RST attributes reduction is proposed and applied to select the most important rules. The selected rules are transformed into fuzzy rules based on discretization cuts of numerical input attributes and simple triangular and trapezoidal membership functions. Fuzzy rules weighing is also proposed and applied based on rules support on the training data. The system is validated using UCI heart disease data sets collected from the U.S., Switzerland and Hungary and data set from Ipoh Specialist Hospital Malaysia. The system is verified by three cardiologists. The results show that the system is able to give the approximate possibility of coronary artery blocking.

show abstract

Section: Methods and Materials Datamentioning

confidence: 99%

Fuzzy Decision Support System for Coronary Artery Disease Diagnosis Based on Rough Set Theory

Setiawan

2014

International Journal of Rough Sets and Data Analysis

View full text Add to dashboard Cite

show abstract

“…Rules can be created by utilize general-to-specific methods or specific-to-general methods. In general-to-specific methods, begin with rule with no antecedent and continue adding conditions until seeing enhancements in evaluation metrics, then, keep on eliminating the conditions of the rule covering pretty specific case [20].…”

Section: Rule-based Classifiermentioning

confidence: 99%

Sentiment Analysis Techniques –Survey

Mirzh,

Zuhair Hussein Ali

2023

wjps

View full text Add to dashboard Cite

Online social media platforms allow people to express their ideas and opinions about anything as products, education, or politics, this produce a huge data requires appropriate tools to analyze it accurately, and that’s why Sentiment Analysis (SA) has become more used in recent years. This paper review the SA and classification methods of machine learning especially supervised techniques like Support Vector Machine (SVM), Neural Networks (NN), Naïve Bayes (NB), Rough Set Theory (RST), Random Forest (RF), Decision Trees (DT), and other technology of SA is lexicon techniques and hybrid methods (which include the lexicon and machine learning techniques). Machine learning consist of two group supervised (Classification and Regression) and unsupervised (clustering and association). The SA Affected from one system to another based on its polarity as “positive” or “negative”, and the language of data that processed.

show abstract

“…Feature Space Mapping networks [14], [15] are based on separable functions, therefore their neurons evaluate the truth of conjunctive logical rules. Products of ST r(X i ; ω i ) soft trapezoidal functions are used in the Incremental Network (IncNet) neural network implemented in the Ghostminer package [16], [17].…”

Section: Relation With Multi-layer Perceptronsmentioning

confidence: 99%

“…Rules were initially generated using C4.5 classification tree [12], and SSV decision tree [13], with another set of crisp rules generated by the Feature Space Mapping (FSM) neurofuzzy network [14], [15] using rectangular membership functions. Both SSV and FSM algorithms are implemented in the Ghostminer data mining package [16] used to generate all results described below. Only simple rules are of interest to psychologists, because each set of rules for a given class has to be commented upon, providing verbal interpretation useful for support of diagnosis.…”

Section: Examples Of Applicationsmentioning

confidence: 99%

“…Analysis of this data made with different methods available in the Ghostminer package [16] showed that using only one single feature all 38 training samples, and all but 3 of the 34 test samples (AML mistaken for ALL cases), are classified correctly. Using SSV decision tree a crisp logical rule based on a single threshold for feature 4847 was found giving such high accuracy.…”

Section: Examples Of Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Uncertainty of Data, Fuzzy Membership Functions, and Multilayer Perceptrons

Duch

2005

IEEE Trans. Neural Netw.

View full text Add to dashboard Cite

Abstract-Probability that a crisp logical rule applied to imprecise input data is true may be computed using fuzzy membership function. All reasonable assumptions about input uncertainty distributions lead to membership functions of sigmoidal shape. Convolution of several inputs with uniform uncertainty leads to bell-shaped Gaussian-like uncertainty functions. Relations between input uncertainties and fuzzy rules are systematically explored and several new types of membership functions discovered. Multi-layered perceptron (MLP) networks are shown to be a particular implementation of hierarchical sets of fuzzy threshold logic rules based on sigmoidal membership functions. They are equivalent to crisp logical networks applied to input data with uncertainty. Leaving fuzziness on the input side makes the networks or the rule systems easier to understand. Practical applications of these ideas are presented for analysis of questionnaire data and gene expression data.Index Terms-Neural networks, multi-layer perceptrons, extraction of logical rules, fuzzy systems, neural output functions.

show abstract

Optimization and Interpretation of Rule-Based Classifiers

Cited by 6 publications

References 7 publications

Fuzzy Decision Support System for Coronary Artery Disease Diagnosis Based on Rough Set Theory

Fuzzy Decision Support System for Coronary Artery Disease Diagnosis Based on Rough Set Theory

Sentiment Analysis Techniques –Survey

Uncertainty of Data, Fuzzy Membership Functions, and Multilayer Perceptrons

Contact Info

Product

Resources

About