Research on Operation Reliability Evaluation of Power-generating Unit

Objective and reasonable evaluation is the basis of power-generating unit optimal operation, but evaluation method based on single weight can’t adapt to the high accuracy requirement of comprehensive evaluation. In order to achieve the accurate evaluation of power-generating unit operating reliability, this paper firstly calculates subjective weight and objective weight of target attribute by analytic hierarchy process method and entropy weight method. It calculates the associated weight of target attribute by grey relational degree method, and it obtains the linear combined weight by relaxation factor. Then, the power-generating unit operating reliability is evaluated by improved TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), and the evaluation result is obtained. Finally, the relaxation factor is continuously evaluated from 0 to 1 to perform sensitivity analysis. This paper evaluates the operation reliability of nine wind turbines in a wind farm, and the evaluation result is closer to actual operation state of wind turbines. This method has high reliability and practical value, and it provides a new technical means for making a reasonable maintenance plan of wind turbines.

Uni- and multivariate probability density models for numeric subgroup discovery

Subgroup Discovery is a supervised, exploratory data mining paradigm that aims to identify subsets of a dataset that show interesting behaviour with respect to some designated target attribute. The way in which such distributional differences are quantified varies with the target attribute type. This work concerns continuous targets, which are important in many practical applications. For such targets, differences are often quantified using z-score and similar measures that compare simple statistics such as the mean and variance of the subset and the data. However, most distributions are not fully determined by their mean and variance alone. As a result, measures of distributional difference solely based on such simple statistics will miss potentially interesting subgroups. This work proposes methods to recognise distributional differences in a much broader sense. To this end, density estimation is performed using histogram and kernel density estimation techniques. In the spirit of Exceptional Model Mining, the proposed methods are extended to deal with multiple continuous target attributes, such that comparisons are not restricted to univariate distributions, but are available for joint distributions of any dimensionality. The methods can be incorporated easily into existing Subgroup Discovery frameworks, so no new frameworks are developed.

Test validity defined as d-connection between target and measured attribute: Expanding the causal definition of Borsboom et al. (2004)

This article suggests a modification to the conception of test validity put forward by Borsboom, Mellenberghand van Heerden (2004). According to the original definition, a test is only valid if test outcomes are causedby variation in the target attribute. According to the d-connection definition of test validity, a test is validfor measuring an attribute if (a) the attribute exists, and (b) variation in the attribute is d-connected tovariation in the measurement outcomes. In other words, a test is valid whenever test outcomes inform useither about what has happened to the target attribute in the past, or about what will happen to the targetattribute in the future. Thus, the d-connection definition expands the number of scenarios in which a test canbe considered valid. Defining test validity as d-connection between target and measured attribute situatesthe validity concept squarely within the structural causal modeling framework of Pearl (2009).

Discovering outstanding subgroup lists for numeric targets using MDL

The task of subgroup discovery (SD) is to find interpretable descriptions of subsets of a dataset that stand out with respect to a target attribute. To address the problem of mining large numbers of redundant subgroups, subgroup set discovery (SSD) has been proposed. State-of-the-art SSD methods have their limitations though, as they typically heavily rely on heuristics and/or user-chosen hyperparameters. We propose a dispersion-aware problem formulation for subgroup set discovery that is based on the minimum description length (MDL) principle and subgroup lists. We argue that the best subgroup list is the one that best summarizes the data given the overall distribution of the target. We restrict our focus to a single numeric target variable and show that our formalization coincides with an existing quality measure when finding a single subgroup, but that---in addition---it allows to trade off subgroup quality with the complexity of the subgroup. We next propose SSD++, a heuristic algorithm for which we empirically demonstrate that it returns outstanding subgroup lists: non-redundant sets of compact subgroups that stand out by having strongly deviating means and small spread.

Pythagoras Tree Applied For Determined Instagram Usage Habit Decision

In previous studies, Pythagoras Tree constructed using the Regression Method, namely ID3 of Standard Deviation Reduction (SDR). The study using SDR for Classification. Data obtained from previous research about Instagram Usage. The result of the study that SDR is useful for the classification method for constructing Pythagoras Tree. However, the target attribute is must use a numerical variable to gain the Standard Deviation and the Mean. Empty data does not affect calculations. Although instances must be discarded, thereby reducing the amount of data. For the case of the Instagram Usage Habit itself, not getting the right pattern to deciding due to the data obtained is less. However, Construct Pythagoras Tree successfully done.

Averaging Attacks on Bounded Noise-based Disclosure Control Algorithms

We describe and evaluate an attack that reconstructs the histogram of any target attribute of a sensitive dataset which can only be queried through a specific class of real-world privacy-preserving algorithms which we call bounded perturbation algorithms. A defining property of such an algorithm is that it perturbs answers to the queries by adding zero-mean noise distributed within a bounded (possibly undisclosed) range. Other key properties of the algorithm include only allowing restricted queries (enforced via an online interface), suppressing answers to queries which are only satisfied by a small group of individuals (e.g., by returning a zero as an answer), and adding the same perturbation to two queries which are satisfied by the same set of individuals (to thwart differencing or averaging attacks). A real-world example of such an algorithm is the one deployed by the Australian Bureau of Statistics’ (ABS) online tool called TableBuilder, which allows users to create tables, graphs and maps of Australian census data [30]. We assume an attacker (say, a curious analyst) who is given oracle access to the algorithm via an interface. We describe two attacks on the algorithm. Both attacks are based on carefully constructing (different) queries that evaluate to the same answer. The first attack finds the hidden perturbation parameter r (if it is assumed not to be public knowledge). The second attack removes the noise to obtain the original answer of some (counting) query of choice. We also show how to use this attack to find the number of individuals in the dataset with a target attribute value a of any attribute A, and then for all attribute values ai ∈ A. None of the attacks presented here depend on any background information. Our attacks are a practical illustration of the (informal) fundamental law of information recovery which states that “overly accurate estimates of too many statistics completely destroys privacy” [9, 15].

EBOC: Ensemble-Based Ordinal Classification in Transportation

Learning the latent patterns of historical data in an efficient way to model the behaviour of a system is a major need for making right decisions. For this purpose, machine learning solution has already begun its promising marks in transportation as well as in many areas such as marketing, finance, education, and health. However, many classification algorithms in the literature assume that the target attribute values in the datasets are unordered, so they lose inherent order between the class values. To overcome the problem, this study proposes a novel ensemble-based ordinal classification (EBOC) approach which suggests bagging and boosting (AdaBoost algorithm) methods as a solution for ordinal classification problem in transportation sector. This article also compares the proposed EBOC approach with ordinal class classifier and traditional tree-based classification algorithms (i.e., C4.5 decision tree, RandomTree, and REPTree) in terms of accuracy. The results indicate that the proposed EBOC approach achieves better classification performance than the conventional solutions.

Two Types of Heuristics in Moral Decision Making

Moral assessment implies ascribing a status of morally wrong, good, etc. (target attribute) to an act. Such an assessment is made on the basis of information about other attributes of the act, including its compliance with the norm, consequences, opinions of others about it, etc. These attributes may be morally relevant (those attributes that an individual could, in the case of rational analysis, recognize as a direct basis for moral assessment) and morally irrelevant (those which would not be recognized in such a status). A comprehensive moral assessment of the target attribute is an assessment based on all morally relevant attributes. A heuristic assessment is based only on a part of morally relevant attributes or based on morally irrelevant attributes. This difference between moral heuristics became the basis for dividing them into two types. Heuristics of the first type implies a simplified assessing of the target attribute based on partial information about morally relevant attributes of an act. The heuristic of the second type operates through a process of attribute substitution when irrelevant attributes are used to assess the target attribute.