Output Effect Evaluation Based on Input Features in Neural Incremental Attribute Learning for Better Classification Performance

[[abstract]]Machine learning is a very important approach to pattern classification. This paper provides a better insight into Incremental Attribute Learning (IAL) with further analysis as to why it can exhibit better performance than conventional batch training. IAL is a novel supervised machine learning strategy, which gradually trains features in one or more chunks. Previous research showed that IAL can obtain lower classification error rates than a conventional batch training approach. Yet the reason for that is still not very clear. In this study, the feasibility of IAL is verified by mathematical approaches. Moreover, experimental results derived by IAL neural networks on benchmarks also confirm the mathematical validation.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]電子

Eine Analyse der Literatur zur Referenzmodellierung im Geschäftsprozessmanagement unter Berücksichtigung quantitativer Methoden

Im Geschäftsprozessmanagement nimmt die Referenzmodellierung bei der Gestaltung von Geschäftsprozessen eine große Bedeutung ein, da auf bereits existierende Modelle zurückgegriffen werden kann. So kann Zeit für die Entwicklung der Prozesse eingespart und von bereits etabliertem Wissen profitiert werden. Die vorliegende Masterarbeit analysiert die Literatur im Bereich der Referenzmodellierung im Geschäftsprozessmanagement unter Berücksichtigung quantitativer Methoden. Es werden insbesondere die Forschungsrichtungen bzw. Themenbereiche, Entwicklungen und der aktuelle Stand der Literatur in diesem Bereich ermittelt. Zunächst werden deutsch- und englischsprachige Artikel nach bestimmten Kriterien ausgewählt. Anschließend folgt eine quantitativ orientierte Analyse der Literatur. Dabei kommt die Latente Semantische Analyse zum Einsatz, mit deren Hilfe Themenbereiche ermittelt werden und die einzelnen Beiträge den ermittelten Themenbereichen zugeordnet werden können. Darüber hinaus wird die Entwicklung der Anzahl der Artikel in den Themenbereichen im Zeitverlauf betrachtet und auf Unterschiede zwischen der deutsch- und englischsprachigen Literatur eingegangen. In der darauf folgenden qualitativ orientierten Analyse werden die Artikel der einzelnen Themenbereiche inhaltlich analysiert und der aktuelle Stand der Forschung dargestellt. Nicht zuletzt werden die Ergebnisse der qualitativen Analyse in Bezug zu den Ergebnissen der quantitativen Analyse gesetzt

Statistical feature ordering for neural-based incremental attribute learning

In pattern recognition, better classification or regression results usually depend on highly discriminative features (also known as attributes) of datasets. Machine learning plays a significant role in the performance improvement for classification and regression. Different from the conventional machine learning approaches which train all features in one batch by some predictive algorithms like neural networks and genetic algorithms, Incremental Attribute Learning (IAL) is a novel supervised machine learning approach which gradually trains one or more features step by step. Such a strategy enables features with greater discrimination abilities to be trained in an earlier step, and avoids interference among relevant features. Previous studies have confirmed that IAL is able to generate accurate results with lower error rates. If features with different discrimination abilities are sorted in different training order, the final results may be strongly influenced. Therefore, the way to sequentially sort features with some orderings and simultaneously reduce the pattern recognition error rates based on IAL inevitably becomes an important issue in this study. Compared with the applicable yet time-consuming contribution-based feature ordering methods which were derived in previous studies, more efficient feature ordering approaches for IAL are presented to tackle classification problems in this study. In the first approach, feature orderings are calculated by statistical correlations between input and output. The second approach is based on mutual information, which employs minimal-redundancy-maximal- relevance criterion (mRMR), a well-known feature selection method, for feature ordering. The third method is improved by Fisher's Linear Discriminant (FLD). Firstly, Single Discriminability (SD) of features is presented based on FLD, which can cope with both univariate and multivariate output classification problems. Secondly, a new feature ordering metric called Accumulative Discriminability (AD) is developed based on SD. This metric is designed for IAL classification with dynamic feature dimensions. It computes the multidimensional feature discrimination ability in each step for all imported features including those imported in previous steps during the IAL training. AD can be treated as a metric for accumulative effect, while SD only measures the one-dimensional feature discrimination ability in each step. Experimental results show that all these three approaches can exhibit better performance than the conventional one-batch training method. Furthermore, the results of AD are the best of the three, because AD is much fitter for the properties of IAL, where feature number in IAL is increasing. Moreover, studies on the combination use of feature ordering and selection in IAL is also presented in this thesis. As a pre-process of machine learning for pattern recognition, sometimes feature orderings are inevitably employed together with feature selection. Experimental results show that at times these integrated approaches can obtain a better performance than non-integrated approaches yet sometimes not. Additionally, feature ordering approaches for solving regression problems are also demonstrated in this study. Experimental results show that a proper feature ordering is also one of the key elements to enhance the accuracy of the results obtained

Assuming Data Integrity and Empirical Evidence to The Contrary

Background: Not all respondents to surveys apply their minds or understand the posed questions, and as such provide answers which lack coherence, and this threatens the integrity of the research. Casual inspection and limited research of the 10-item Big Five Inventory (BFI-10), included in the dataset of the World Values Survey (WVS), suggested that random responses may be common. Objective: To specify the percentage of cases in the BRI-10 which include incoherent or contradictory responses and to test the extent to which the removal of these cases will improve the quality of the dataset. Method: The WVS data on the BFI-10, measuring the Big Five Personality (B5P), in South Africa (N=3 531), was used. Incoherent or contradictory responses were removed. Then the cases from the cleaned-up dataset were analysed for their theoretical validity. Results: Only 1 612 (45.7%) cases were identified as not including incoherent or contradictory responses. The cleaned-up data did not mirror the B5P- structure, as was envisaged. The test for common method bias was negative. Conclusion: In most cases the responses were incoherent. Cleaning up the data did not improve the psychometric properties of the BFI-10. This raises concerns about the quality of the WVS data, the BFI-10, and the universality of B5P-theory. Given these results, it would be unwise to use the BFI-10 in South Africa. Researchers are alerted to do a proper assessment of the psychometric properties of instruments before they use it, particularly in a cross-cultural setting

Leading Towards Voice and Innovation: The Role of Psychological Contract

Background: Empirical evidence generally suggests that psychological contract breach (PCB) leads to negative outcomes. However, some literature argues that, occasionally, PCB leads to positive outcomes. Aim: To empirically determine when these positive outcomes occur, focusing on the role of psychological contract (PC) and leadership style (LS), and outcomes such as employ voice (EV) and innovative work behaviour (IWB). Method: A cross-sectional survey design was adopted, using reputable questionnaires on PC, PCB, EV, IWB, and leadership styles. Correlation analyses were used to test direct links within the model, while regression analyses were used to test for the moderation effects. Results: Data with acceptable psychometric properties were collected from 11 organisations (N=620). The results revealed that PCB does not lead to substantial changes in IWB. PCB correlated positively with prohibitive EV, but did not influence promotive EV, which was a significant driver of IWB. Leadership styles were weak predictors of EV and IWB, and LS only partially moderated the PCB-EV relationship. Conclusion: PCB did not lead to positive outcomes. Neither did LS influencing the relationships between PCB and EV or IWB. Further, LS only partially influenced the relationships between variables, and not in a manner which positively influence IWB