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Accurate and credible software effort estimation is a challenge for academic research and software industry. From many software effort estimation models in existence, Estimation by Analogy (EA) is still one of the preferred techniques by software engineering practitioners because it mimics the human problem solving approach. Accuracy of such a model depends(More)
Context: Software effort estimation at early stage is a crucial task for project bedding and feasibility study. Since collected data at early stage of software development lifecycle is always imprecise and uncertain, it is very hard to deliver accurate estimate. Analogy-based estimation, which is one of the popular estimation methods, is rarely used at(More)
One of the major problems with software project management is the difficulty to predict accurately the required effort for developing software applications. Analogy Software effort estimation appears well suited to model problems of this nature. The analogy approach may be viewed as a systematic development of the expert opinion through experience learning(More)
A framework of new unified neural and neuro-fuzzy approaches for integrating implicit and explicit knowledge in neuro-symbolic systems is proposed. In the developed hybrid system, training data set is used for building neuro-fuzzy modules, and represents implicit domain knowledge. On the other hand, the explicit domain knowledge is represented by fuzzy(More)
Model interpretation is one of the key aspects of the model evaluation process. The explanation of the relationship between model variables and outputs is relatively easy for statistical models, such as linear regressions, thanks to the availability of model parameters and their statistical significance. For " black box " models, such as random forest, this(More)
The OECD has proposed five principles for validation of QSAR models used for regulatory purposes. Here we present a case study investigating how these principles can be applied to models based on Kohonen and counter propagation neural networks. The study is based on a counter propagation network model that has been built using toxicity data in fish fathead(More)
The increasing amount and complexity of data in toxicity prediction calls for new approaches based on hybrid intelligent methods for mining the data. This focus is required even more in the context of increasing number of different classifiers applied in toxicity prediction. Consequently, there exist a need to develop tools to integrate various approaches.(More)
The paper focuses on the problem of rule extraction from neural networks, with the aim of transforming the knowledge captured in a trained neural network into a familiar form for human user. The ultimate purpose for us is to develop human friendly shells for neural network based systems. In the first part of the paper it is presented an approach on(More)
In this study, results of a variety of ML algorithms are tested against artificially polluted datasets with noise. Two noise models are tested, each of these studied on a range of noise levels from 0 to 50algorithm, a linear regression algorithm, a decision tree, a M5 algorithm, a decision table classifier, a voting interval scheme as well as a hyper pipes(More)