From Elections to Democracy: Building Accountable Government in Hungary and Poland by Susan Rose‐Ackerman

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97210/1/j.1538-165X.2006.tb01462.x.pd

A Comparison of Four Approaches to Discretization Based on Entropy †

We compare four discretization methods, all based on entropy: the original C4.5 approach to discretization, two globalized methods, known as equal interval width and equal frequency per interval, and a relatively new method for discretization called multiple scanning using the C4.5 decision tree generation system. The main objective of our research is to compare the quality of these four methods using two criteria: an error rate evaluated by ten-fold cross-validation and the size of the decision tree generated by C4.5. Our results show that multiple scanning is the best discretization method in terms of the error rate and that decision trees generated from datasets discretized by multiple scanning are simpler than decision trees generated directly by C4.5 or generated from datasets discretized by both globalized discretization methods

The usefulness of a machine learning approach to knowledge acquisition

This paper presents results of experiments showing how machine learning methods are useful for rule induction in the process of knowledge acquisition for expert systems. Four machine learning methods were used: ID3, ID3 with dropping conditions, and two options of the system LERS (Learning from Examples based on Rough Sets): LEM1 and LEM2. Two knowledge acquisition options of LERS were used as well. All six methods were used for rule induction from six real-life data sets. The main objective was to test how an expert system, supplied with these rule sets, performs without information on a few attributes. Thus an expert system attempts to classify examples with all missing values of some attributes. As a result of experiments, it is clear that all machine learning methods performed much worse than knowledge acquisition options of LERS. Thus, machine learning methods used for knowledge acquisition should be replaced by other methods of rule induction that will generate complete sets of rules. Knowledge acquisition options of LERS are examples of such appropriate ways of inducing rules for building knowledge bases

Global discretization of continuous attributes as preprocessing for machine learning

AbstractReal-life data usually are presented in databases by real numbers. On the other hand, most inductive learning methods require a small number of attribute values. Thus it is necessary to convert input data sets with continuous attributes into input data sets with discrete attributes. Methods of discretization restricted to single continuous attributes will be called local, while methods that simultaneously convert all continuous attributes will be called global. In this paper, a method of transforming any local discretization method into a global one is presented. A global discretization method, based on cluster analysis, is presented and compared experimentally with three known local methods, transformed into global. Experiments include tenfold cross-validation and leaving-one-out methods for ten real-life data sets

Reduced Data Sets and Entropy-Based Discretization

This work is licensed under a Creative Commons Attribution 4.0 International License.Results of experiments on numerical data sets discretized using two methods—global versions of Equal Frequency per Interval and Equal Interval Width-are presented. Globalization of both methods is based on entropy. For discretized data sets left and right reducts were computed. For each discretized data set and two data sets, based, respectively, on left and right reducts, we applied ten-fold cross validation using the C4.5 decision tree generation system. Our main objective was to compare the quality of all three types of data sets in terms of an error rate. Additionally, we compared complexity of generated decision trees. We show that reduction of data sets may only increase the error rate and that the decision trees generated from reduced decision sets are not simpler than the decision trees generated from non-reduced data sets

Partition triples: A tool for reduction of data sets

Data sets discussed in this paper are presented as tables with rows corresponding to examples (entities, objects) and columns to attributes. A partition triple is defined for such a table as a triple of partitions on the set of examples, the set of attributes, and the set of attribute values, respectively, preserving the structure of a table. The idea of a partition triple is an extension of the idea of a partition pair, introduced by J. Hartmanis and J. Steams in automata theory. Results characterizing partition triples and algorithms for computing partition triples are presented. The theory is illustrated by an example of an application in machine learning from examples. (C) 1996 Academic Press, Inc

Partition triples: A tool for reduction of data sets

Data sets discussed in this paper are presented as tables with rows corresponding to examples (entities, objects) and columns to attributes. A partition triple is defined for such a table as a triple of partitions on the set of examples, the set of attributes, and the set of attribute values, respectively, preserving the structure of a table. The idea of a partition triple is an extension of the idea of a partition pair, introduced by J. Hartmanis and J. Steams in automata theory. Results characterizing partition triples and algorithms for computing partition triples are presented. The theory is illustrated by an example of an application in machine learning from examples. (C) 1996 Academic Press, Inc

Improving prediction of preterm birth using a new classification scheme and rule induction

Prediction of preterm birth is a poorly understood domain. The existing manual methods of assessment of preterm birth are 17% - 38% accurate. The machine learning system LERS was used for three different datasets about pregnant women. Rules induced by LERS were used in conjunction with a classification scheme of LERS, based on ''bucket brigade algorithm'' of genetic algorithms and enhanced by partial matching. The resulting prediction of preterm birth in new, unseen cases is much more accurate (68%-90%)

Global discretization of continuous attributes as preprocessing for machine learning

Real-life data usually are presented in databases by real numbers. On the other hand, most inductive learning methods require a small number of attribute values. Thus it is necessary to convert input data sets with continuous attributes into input data sets with discrete attributes. Methods of discretization restricted to single continuous attributes will be called local, while methods that simultaneously convert all continuous attributes will be called global. in this paper, a method of transforming any local discretization method into a global one is presented. A global discretization method, based on cluster analysis is presented and compared experimentally with three known local methods, transformed into global. Experiments include tenfold cross-validation and leaving-one-out methods for ten real-life data sets

Using EEG measures to quantify reduced daytime vigilance in patients diagnosed with obstructive sleep apnoea using a novel electroencephalogram analysis method

Introduction Vigilance in obstructive sleep apnoea (OSA) does not correlate well with disease severity/ symptoms: Hence the need for a simple objective test. One such method could be quantitative analysis of the awake electroencephalogram (qEEG). qEEG is conventionally analysed using Power Spectral Analysis (PSA) looking at different EEG frequencies of delta, theta, alpha and beta. A novel method of analyzing the qEEG: De-trended fluctuation analysis (DFA) provides a single value: the scaling exponent (SE), which measures the fluctuations in the EEG signal. Artefact removal from qEEG is mandatory with the gold standard being manual scoring. Another method of automated artefact removal is independent component analysis (ICA). Objective Investigate the role of PSA and DFA (SE) as an objective measure of testing vigilance and validate the use of ICA in patients diagnosed with OSA. Methodology Retrospective cross-sectional study of untreated OSA patients. Results ICA and manual artefact removal gave well-correlated results in the DFA (SE), but not PSA. EEG slowing measured by PSA and DFA did not correlate to impaired performance during a battery of 14 separate performance tests. Conclusion ICA and manual artefact removal can be interchangeably used in extracting DFA measurements with confidence. In PSA metrics the use of ICA may not be reliable