Terms Of Decision Rules Research Articles

Causal Inference and Machine Learning approaches to discover de novo sub-populations with heterogeneous air pollution health effects

BACKGROUND AND AIM: In environmental health sciences, it is critically important to identify subgroups of the study population where a treatment (or exposure) has a notably larger or smaller causal effect on an outcome compared to the population average. METHODS: In this paper, we propose a new causal rule ensemble (CRE) method that: 1) discovers de novo subgroups with significantly heterogeneous treatment effects (i.e., causal rules); 2) ensures interpretability of these subgroups because they are defined in terms of decision rules; and 3) estimates the causal effect for each of these newly discovered subgroups with small bias and high statistical precision. We also introduce a new sensitivity analysis to unmeasured confounding bias. RESULTS:We apply the proposed CRE method to the Medicare data to study the heterogeneous effects of long-term exposure to PM2.5 on 5-year mortality. The population consists of Medicare beneficiaries in New England regions in the United States between 2000 and 2006. The treatment is whether the two-year (2000-2001) average of exposure to PM2.5 is greater than 12μg/m3. The outcome is five-year mortality measured between 2002-2006. We found that younger than 80 years old and not eligible for Medicaid are significantly less vulnerable than the baseline subgroup. Results were not sensitive to unmeasured confounding bias. CONCLUSIONS:We introduce a new method for studying treatment effect heterogeneity that notably improves interpretability in terms of causal rules. The proposed CRE methodology provides a more stable approach to discover and estimate heterogeneous effects while maintaining high levels of interpretability. KEYWORDS: Air pollution, causal inference, mortality, methodological study design

Optimal Neuro-Fuzzy Equalizers for Detecting Nonlinear Distortion Channels of the Perpendicular Magnetic Recording System

Nonlinear distortions caused by partial erasure and nonlinear transition shifts interacting with inter-symbol interference, are a major hindrance to data storage systems, since they degrade detector performance. This work aims to design and optimize the neuro-fuzzy equalizer (NFE) using the multi-objective genetic algorithm (MOGA) to detect nonlinear high-density magnetic recording (MR) channels. Through the GA-assisted back-propagation algorithm and least mean square optimization, the complexity in terms of decision rules is reduced by 25% and significantly provides 65% lower signal processing computation. When applied to the perpendicular (MR) system, the proposed NFE outperforms existing equalizers such as the neural network-based equalizer, fuzzy logic equalizer, and conventional NFE for the Volterra and jitter media noise channels using 1–3 dB and 1.5–3.5 dB signal-to-noise ratio gains at the bit-error-rate of 10-4, respectively. Furthermore, compared to the other models, the NFE provides a more effective output mean square error performance for retrieving the original bit data.

Putting Dominance-based Rough Set Approach and robust ordinal regression together

We propose to apply the Dominance-based Rough Set Approach (DRSA) on the results of multiple criteria decision aiding (MCDA) methods, in order to explain their recommendations in terms of rules involving conditions on evaluation criteria. The rules represent a decision model which is transparent and easy to interpret for the DM. In fact, decision rules give arguments to justify and explain the decision and, in a learning perspective, they can be the starting point for an interactive procedure for analyzing and constructing the DM's preferences. It enables his/her understanding of the conditions for the suggested recommendation, and provides useful information about the role of particular criteria or their subsets. DRSA can be used in junction with any MCDA method producing a classification result or a preference relation in the set of alternatives. In this paper, we apply DRSA to a recently proposed MCDA methodology, called Robust Ordinal Regression (ROR). The ROR approach to MCDA, also called disaggregation–aggregation approach, aims at inferring parameters of a preference model representing some holistic preference comparisons of alternatives provided by the decision maker (DM). Contrary to the usual ordinal regression approaches to MCDA, ROR takes into account the whole set of possible value of preference model parameters compatible with the DM's preference information, to work out a final recommendation. In consequence, ROR gives a recommendation in terms of necessary and possible consequences of the application of all the compatible sets of parameter values to the considered set of alternatives. UTAGMS and GRIP methods apply this approach, considering general monotonic additive value functions, and produce as a result the necessary and possible preference relations. In this paper we show how DRSA completes the decision aiding process started with ROR, providing a very useful interpretation of the preference relations in terms of decision rules.

Short-medium Term Tourist Services Demand Forecasting with Rough Set Theory

The need to understand more in depth tourism demand trends and the aim to provide the tourist operators and the policy makers with innovative predicting tools are the key points of our research. In tourism literature predicting tourist demand has become a flourishing theme of research at a macroeconomic level, while the study is still lacking at a microeconomic level. Our attention is focused on analyzing Italian tourists’ behaviours on the basis of statistical surveys on households, life conditions, incomes, consumptions, travels and holidays. Data analysis is performed by means of Rough Sets Theory, a Data Mining technique which, unlike more traditional time-series and econometric models, can easily manage categorical variables. Data were provided by GfK Eurisko and concern social, cultural and behavioural trends in Italy, collected by means of a psychographic survey. Some interesting relations between consumer behaviours and corresponding tourism consumption choices are obtained in terms of decision rules.

Understanding Change and Stability in Party Ideologies: Do Parties Respond to Public Opinion or to Past Election Results?

Previous research explains the evolution of parties' ideological positions in terms of decision rules that stress the uncertainty of the political environment. The authors extend this research by examining whether parties adjust their ideologies in response to two possible influences: shifts in public opinion, and past election results. Their empirical analyses, which are based on the Comparative Manifesto Project's codings of parties' post-war programmes in eight West European nations, suggest that parties respond to shifts in public opinion, but that these effects are only significant in situations where public opinion is clearly shifting away from the party's policy positions. By contrast, no evidence is found here that parties adjust their ideologies in response to past election results. These findings have important implications for parties' election strategies and for models of political representation.

Can Bayesian confirmation measures be useful for rough set decision rules?

Bayesian confirmation theory considers a variety of non-equivalent confirmation measures which say in what degree a piece of evidence confirms a hypothesis. In this paper, we apply some well-known confirmation measures within the rough set approach to discovering relationships in data in terms of decision rules. Moreover, we discuss some interesting properties of these confirmation measures and we propose a new property of monotonicity that is particularly relevant within rough set approach. The main result of this paper states that only two from among confirmation measures considered in the literature have the desirable properties from the viewpoint of the rough set approach. Moreover, we clarify relationships between logical implications and decision rules, and we compare the confirmation measures to several related measures, like independence (dependence) of logical formulas, interestingness measures in data mining and Bayesian solutions of raven's paradox.

Application of the Rough Set Approach to Evaluation of Bankruptcy Risk

AbstractWe present a new approach to evaluation of bankruptcy risk of firms based on the rough set theory. The concept of a rough set appeared to be an effective tool for the analysis of information systems representing knowledge gained by experience. The financial information system describes a set of objects (firms) by a set of multi‐valued attributes (financial ratios and qualitative variables), called condition attributes. The firms are classified into groups of risk subject to an expert's opinion, called decision attribute. A natural problem of knowledge analysis consists then in discovering relationships, in terms of decision rules, between description of firms by condition attributes and particular decisions. The rough set approach enables one to discover minimal subsets of condition attributes ensuring an acceptable quality of classification of the firms analysed and to derive decision rules from the financial information system which can be used to support decisions about financing new firms. Using the rough set approach one analyses only facts hidden in data, it does not need any additional information about data and does not correct inconsistencies manifested in data; instead, rules produced are categorized into certain and possible. A real problem of the evaluation of bankruptcy risk by a Greek industrial development bank is studied using the rough set approach.

The limits of path error-neglecting in straight lane driving

Abstract It is argued that driving cannot simply be considered as a permanent, closed-loop task. Time-to-line-crossing (TLC) is used as a measure to quantify the potential role of visual open-loop and path-error-neglecting strategies. Basically, TLC represents the time available for a driver to neglect path errors until the moment at which any part of the vehicle reaches one of the lane boundaries. The strategy adopted by drivers during error-neglecting should be represented in terms of decision rules, describing how drivers switch from error-neglecting to error-correcting when approaching the edge of a lane. The experiment to be presented in this paper was designed to provide these rules for a straight lane-keeping task. Drivers were instructed to neglect the vehicle path error and to switch to error-correcting only at that moment when the vehicle heading could still comfortably be corrected to prevent a crossing of the lane boundary. The results show that the lateral distance from the lane boundary at which drivers switch to error-correction increases about linearly with the lateral approach speed. This mechanism results in an approximately constant TLC (time) distance at the moment of decision: this result being consistent over a broad range of speeds.

Decision Rules and Transactions, Organizations and Markets

Organizations and markets are defined as sets of people who are connected together by the logical orders of decision rule and transaction respectively. Super organizations are defined as sets of organizations connected by decision rules. The logical order is the basis of an arrangement of people or organizations. These concepts are developed in an unambiguous manner, and used to produce analytically useful definitions of pure and hybrid classes. The concepts are shown to be of value in the analysis of organizations and markets. The concept of the decision rule is used to define static organization structures, and to define dynamic task processes in them. Generalizations about organizations and markets are developed and put together to get new ones. Two classification schemes in the literature are simplified by a redefinition of their classes in terms of decision rules only. This not only integrates two seemingly different schemes, but it illustrates how useful the concept of the decision rule could be in analyzing and generalizing about organizations, other arrangements of persons, and arrangements of arrangements of persons.