Many decision tree algorithms were proposed over the last few decades. A lack of publishing standards for decision tree algorithm software produced a large time gap between algorithm proposals and their wider application in practice. Non-existence of common repository for storing algorithms and their parts led to a need to re-implement these algorithms from a scratch when they had to be implemented on a different platform. This makes the comparison between algorithms and their partial improvements vague. In addition, combinations and interactions between different algorithm parts haven't been analyzed thoroughly. Reusable component design of decision tree algorithms has been recently suggested as a potential solution to these problems. In this paper we describe an architecture for component-based (white-box) decision tree algorithm design, and we present an open-source framework which enables design and fair testing of decision tree algorithms and their parts. This architecture and developed platform can provide the research community with a common codebase for storing, designing, and evaluating decision tree algorithms (traditional, multivariate and hybrid) and their partial improvements. It is intended for data mining practitioners, algorithm and software developers, and as well for students, as a technology enhanced learning tool.
Analyzing and determining patterns among indicators of academic success and their correlation to students' enrollment status can be a good foundation in the process of adapting and improving the curriculum of higher education institutions, according to students' characteristics. In this paper we analyzed variables (the completed study program, student's gender, type of previously finished secondary school and the region where they finished secondary school) and indicators of academic success (GPA and study time) and their dependence on the status of enrollment at university. The results of this research indicate that students who enrolled in the status of budget financing (SBF) achieve greater success in terms of GPA (higher GPA) and the average time required to complete the study (shorter average time of study) in comparison to students who enrolled in the faculty in the self-financing status.
Innovation and innovative competences have long been recognized to be one of the basic competitiveness and long-term profitability factors, from the firm level up to national and global economy level. Having in mind that innovation, generally, represents the process from idea to realization, the paper is concerned with the key elements of innovation process models in today's knowledge-driven economy. Contemporary, network and integrative innovation models should be based on the relation between the firm, strategy and environment i.e. the firm should be linked with the environment by means of strategy, which represents a mediating force-the firm is responding to market requests driven by its strategic concept. Integrative models for 21 st century should overcome basic weaknesses of conventional linear technology-push and market-pull approach, taking into account the possibilities of proactive innovation strategy, networking and external linkages of the firm.
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