Hybrid methods to select informative gene sets in microarray data classification

One of the key applications of microarray studies is to select and classify gene expression profiles of cancer and normal subjects. In this study, two hybrid approaches–genetic algorithm with decision tree (GADT) and genetic algorithm with neural network (GANN)–are utilized to select optimal gene sets which contribute to the highest classification accuracy. Two benchmark microarray datasets were tested, and the most significant disease related genes have been identified. Furthermore, the selected gene sets achieved comparably high sample classification accuracy (96.79% and 94.92% in colon cancer dataset, 98.67% and 98.05% in leukemia dataset) compared with those obtained by mRMR algorithm. The study results indicate that these two hybrid methods are able to select disease related genes and improve classification accuracy.

Discovering frequent sets from data streams with CPU constraint

Data streams are usually generated in an online fashion characterized by huge volume, rapid unpredictable rates, and fast changing data characteristics. It has been hence recognized that mining over streaming data requires the problem of limited computational resources to be adequately addressed. Since the arrival rate of data streams can significantly increase and exceed the CPU capacity, the machinery must adapt to this change to guarantee the timeliness of the results. We present an online algorithm to approximate a set of frequent patterns from a sliding window over the underlying data stream - given apriori CPU capacity. The algorithm automatically detects overload situations and can adaptively shed unprocessed data to guarantee the timely results. We theoretically prove, using probabilistic and deterministic techniques, that the error on the output results is bounded within a pre-specified threshold. The empirical results on various datasets also confirmed the feasiblity of our proposal.

EStream: online mining of frequent sets with precise error guarantee

In data stream applications, a good approximation obtained in a timely  manner is often better than the exact answer that’s delayed beyond the window of opportunity. Of course, the quality of the approximate is as important as its timely delivery. Unfortunately, algorithms capable of online processing do not conform strictly to a precise error guarantee. Since online processing is essential and so is the precision of the error, it is necessary that stream algorithms meet both criteria. Yet, this is not the case for mining frequent sets in data streams. We present EStream, a novel algorithm that allows online processing while producing results strictly within the error bound. Our theoretical and experimental results show that EStream is a better candidate for finding frequent sets in data streams, when both constraints need to be satisfied.

Online mining of frequent sets in data streams with error guarantee

For most data stream applications, the volume of data is too huge to be stored in permanent devices or to be thoroughly scanned more than once. It is hence recognized that approximate answers are usually sufficient, where a good approximation obtained in a timely manner is often better than the exact answer that is delayed beyond the window of opportunity. Unfortunately, this is not the case for mining frequent patterns over data streams where algorithms capable of online processing data streams do not conform strictly to a precise error guarantee. Since the quality of approximate answers is as important as their timely delivery, it is necessary to design algorithms to meet both criteria at the same time. In this paper, we propose an algorithm that allows online processing of streaming data and yet guaranteeing the support error of frequent patterns strictly within a user-specified threshold. Our theoretical and experimental studies show that our algorithm is an effective and reliable method for finding frequent sets in data stream environments when both constraints need to be satisfied.

Admissible Christianity : a study of Christian fundamentalism in the writings of James Barr

An attempt to set forth the essential nature of a theology is a notoriously difficult task. This thesis addresses two questions to a contemporary study of fundamentalism. It asks to what extent has James Barr been able to describe the theology of fundamentalism and to what extent his critical analysis of that theology is philosophically valid? The first chapter identifies the inherent difficulties in a phenomenology of fundamentalism and includes an historical survey of the theology of the movement. This chapter is supported by appendix one which identifies the philosophical culture associated with fundamentalist thought. Barr's description of the theological and religious character of fundamentalism is accepted within the identified limitations. The second and third chapters give an account of Barr's theological evaluation of fundamentalism. He argues the fundamentalists espouse an aberrant form of Christianity. Their religion represents a projection onto the biblical text of a religion foreign to the theological character of the Old and New Testaments. This projection is achieved by an intellectually sophisticated hermeneutical procedure. The doctrines of inerrancy, verbal inspiration and infallibility establish an understanding of Christianity which does not represent the essential character of the Christian faith. Fundamentalist hermeneutics, Barr concludes, allows for a theology indigenous neither to the biblical text nor to the Christian tradition. It attempts to afford biblical justification to the doctrines of a human religion extraneous to the biblical text. The fourth chapter considers the philosophical basis of Barr's understanding of the Bible. He takes the idealist view that the biblical text possesses a theological meaning whose boundaries can be delineated and whose essential content defined. This chapter is supported by appendix two which locates Barr's writings on fundamentalism within his wider concerns about the hermeneutical problems raised by the biblical text and the religious authority of the Bible. The penultimate chapter surveys the insights of contemporary literary theory concerning the perception of written texts. The philosophical validity of an idealist view of the biblical text is questioned. Two major conclusions are drawn. Barr's assessment of fundamentalism is philosophically dependent upon his idealist perception of the biblical text. This conclusion leads to the more general conclusion that the biblical text contains no essential description of Christianity but is capable of being read according to a range of theological interpretations some of which are more defensible than others.

Matrices representation of multi soft-sets and its application

In previous paper, we introduced a concept of multi-soft sets and used it for finding reducts. However, the comparison of the proposed reduct has not been presented yet, especially with rough-set based reduct. In this paper, we present matrices representation of multi-soft sets. We define AND and OR operations on a collection of such matrices and apply it for finding reducts and core of attributes in a multi-valued information system. Finally, we prove that our proposed technique for reduct is equivalent to Pawlak’s rough reduct.

Analysis on relationship between extreme pathways and correlated reaction sets

Background: Constraint-based modeling of reconstructed genome-scale metabolic networks has been successfully applied on several microorganisms. In constraint-based modeling, in order to characterize all allowable phenotypes, network-based pathways, such as extreme pathways and elementary flux modes, are defined. However, as the scale of metabolic network rises, the number of extreme pathways and elementary flux modes increases exponentially. Uniform random sampling solves this problem to some extent to study the contents of the available phenotypes. After uniform random sampling, correlated reaction sets can be identified by the dependencies between reactions derived from sample phenotypes. In this paper, we study the relationship between extreme pathways and correlated reaction sets.

Results: Correlated reaction sets are identified for E. coli core, red blood cell and Saccharomyces cerevisiae metabolic networks respectively. All extreme pathways are enumerated for the former two metabolic networks. As for Saccharomyces cerevisiae metabolic network, because of the large scale, we get a set of extreme pathways by sampling the whole extreme pathway space. In most cases, an extreme pathway covers a correlated reaction set in an 'all or none' manner, which means either all reactions in a correlated reaction set or none is used by some extreme pathway. In rare cases, besides the 'all or none' manner, a correlated reaction set may be fully covered by combination of a few extreme pathways with related function, which may bring redundancy and flexibility to improve the survivability of a cell. In a word, extreme pathways show strong complementary relationship on usage of reactions in the same correlated reaction set.

Conclusion: Both extreme pathways and correlated reaction sets are derived from the topology information of metabolic networks. The strong relationship between correlated reaction sets and extreme pathways suggests a possible mechanism: as a controllable unit, an extreme pathway is regulated by its corresponding correlated reaction sets, and a correlated reaction set is further regulated by the organism's regulatory network.

Continuous product innovation : a comparison of key elements across different contingency sets

This paper discusses results from an international study of continuous improvement in product innovation. The empirical research is based upon a theoretical model of continuous product innovation (CPI) that identifies contingencies, behaviours, levers and performances relevant to improving product innovation processes. As successful knowledge management is widely recognised as a key capability for firms to successfully develop CPI, companies have been classified according to identified contingencies and the impact of these contingencies on key knowledge management criteria. Comparative analysis of the identified groups of companies has demonstrated important differences between the learning behaviours found present in the two groups thus identified, and in the levers used to develop and support these behaviours. The selection of performance measures by the two groups has highlighted further significant differences in the way the two groups understand and measure their CPI processes. Finally, the paper includes a discussion of appropriate mechanisms for firms with similar contingency sets to improve their approaches to organisational learning and product innovation.

A construction method of Atanassov’s intuitionistic fuzzy sets for image processing

In this work we introduce a new construction method of Atanassov's intuitionistic fuzzy sets (A-IFSs) from fuzzy sets. We use A-IFSs in image processing. We propose a new image magnification algorithm using A-IFSs. This algorithm is characterized by its simplicity and its efficiency.

Missing value estimation for mixed-attribute data sets

Missing data imputation is a key issue in learning from incomplete data. Various techniques have been developed with great successes on dealing with missing values in data sets with homogeneous attributes (their independent attributes are all either continuous or discrete). This paper studies a new setting of missing data imputation, i.e., imputing missing data in data sets with heterogeneous attributes (their independent attributes are of different types), referred to as imputing mixed-attribute data sets. Although many real applications are in this setting, there is no estimator designed for imputing mixed-attribute data sets. This paper first proposes two consistent estimators for discrete and continuous missing target values, respectively. And then, a mixture-kernel-based iterative estimator is advocated to impute mixed-attribute data sets. The proposed method is evaluated with extensive experiments compared with some typical algorithms, and the result demonstrates that the proposed approach is better than these existing imputation methods in terms of classification accuracy and root mean square error (RMSE) at different missing ratios.