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This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique. Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition. This book invites readers to explore the many benefits in data mining that decision trees offer:

The digital era introduces a significant issue concerning the preservation of individuals’ privacy. Each individual has two autonomous traits, privacy concern which indicates how anxious that person is about preserving privacy, and privacy behavior which refers to the actual actions the individual takes to preserve privacy. The significant gap between these two traits is called the privacy paradox. While the existence and the extensive distribution of the privacy paradox is widely-considered in both academic and public discussion, no convincing explanation of the phenomenon has been provided. In this study we harness a new mathematical approach, “soft logic,” to better represent the reality of the privacy paradox. Soft numbers extend zero from a singularity to an infinite one-dimensional axis, thus enabling the representation of contradictory situations that exist simultaneously, i.e., a paradox. We develop a mathematical model for representing the privacy paradox with soft numbers, and demonstrate its application empirically. This new theory has the potential to address domains that mix soft human reality with robust technological reality.

This manuscript presents an innovative approach to the concept of sustainability in the realm of Artificial Intelligence (AI), recognizing that sustainability is a dynamic vision characterized by harmony and balance. We argue that achieving sustainability in AI systems requires moving beyond rigid adherence to protocols and compliance checklists, which tend to simplify sustainability into static criteria. Instead, sustainable AI should reflect the balance and adaptability intrinsic to the broader vision of sustainability. In crafting this vision, we draw upon the principles of complex systems theory, the wisdom of philosophical doctrines, and the insights of ecology, weaving them into a comprehensive paradigm.

Background Academic Medical Centers (AMCs) must simultaneously serve different purposes: Delivery of high quality healthcare services to patients, as the main mission, supported by other core missions such as academic activities, i.e., researching, teaching and tutoring, while maintaining solvency. This study aims to develop a methodology for constructing models evaluating the academic value provided by AMCs and implementing it at the largest AMC in Israel. Methods Thirty five practiced educators and researchers, academic experts, faculty members and executives, all employed by a metropolitan 1500-bed AMC, were involved in developing academic quality indicators. First, an initial list of AMCs’ academic quality indicators was drafted, using a literature review and consulting scholars. Afterwards, additional data and preferences were collected by conducting semi-structured interviews, complemented by a three-round Delphi Panel. Finally, the methodology for constructing a model evaluating the academic value provided by the AMC was developed. Results The composite academic quality indicators methodology consists of nine indicators (relative weight in parentheses): ‘Scientific Publications Value ’ (18.7%), ‘Completed Studies ’ (13.5%), ‘Authors Value ’ (13.0%), ‘Residents Quality ’ (11.3%), ‘Competitive Grants Budget ’ (10.2%), ‘Academic Training’ (8.7%), ‘Academic Positions’ (8.3%), ‘Number of Studies’ (8.3%) and ‘Academic Supervision’ (8.0%). These indicators were grouped into three core categories: ‘Education ’, ‘Research’ and ‘ Publications ’, having almost the same importance on a scale from zero to one (0–1), i.e., 0.363, 0.320, and 0.317, respectively. The results demonstrated a high level of internal consistency (Cronbach-alpha range: 0.79–0.86). Conclusions We have found a gap in the ability to measure academic value provided by AMCs. The main contribution of this research is the development of methodology for constructing evaluation models for AMCs academic performance. Further studies are needed to further test the validity and reliability of the proposed methodology at other sites.

Improving manufacturing quality is an important challenge in various industrial settings. Data mining methods mostly approach this challenge by examining the effect of operation settings on product quality. We analyze the impact of operational sequences on product quality. For this purpose, we propose a novel method for visual analysis and classification of operational sequences. The suggested framework is based on an Iterated Function System (IFS), for producing a fractal representation of manufacturing processes. We demonstrate our method with a software application for visual analysis of quality-related data. The proposed method offers production engineers an effective tool for visual detection of operational sequence patterns influencing product quality, and requires no understanding of mathematical or statistical algorithms. Moreover, it enables to detect faulty operational sequence patterns of any length, without predefining the sequence pattern length. It also enables to visually distinguish between different faulty operational sequence patterns in cases of recurring operations within a production route. Our proposed method provides another significant added value by enabling the visual detection of rare and missing operational sequences per product quality measure. We demonstrate cases in which previous methods fail to provide these capabilities.

This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique. Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns.

We strive to understand, by using mathematical tools, the phenomena where the observer can interpret simultaneously opposite situations, such as the distinct interpretations of the Necker cube. In this study we present a new coordinate system that draws a distinction between -0 and +0. The central theorem presented here postulates that when divisions and multiplications of 0 are combined with real numbers, the Mobius strip provides a model for non-standard analysis. We also suggest that the axis 0 is the fifth dimension extension of the quaternions. We also propose to add the soft logic capability to humanoid type robots, as a way to overcome the Turing test.

Background Cobalt-ferrite nanoparticles (Co-Fe NPs) are attractive for nanotechnology-based therapies. Thus, exploring their effect on viability of seven different cell lines representing different organs of the human body is highly important. Methods The toxicological effects of Co-Fe NPs were studied by in-vitro exposure of A549 and NCIH441 cell-lines (lung), precision-cut lung slices from rat, HepG2 cell-line (liver), MDCK cell-line (kidney), Caco-2 TC7 cell-line (intestine), TK6 (lymphoblasts) and primary mouse dendritic-cells. Toxicity was examined following exposure to Co-Fe NPs in the concentration range of 0.05 -1.2 mM for 24 and 72 h, using Alamar blue, MTT and neutral red assays. Changes in oxidative stress were determined by a dichlorodihydrofluorescein diacetate based assay. Data analysis and predictive modeling of the obtained data sets were executed by employing methods of Knowledge Discovery from Data with emphasis on a decision tree model (J48). Results Different dose–response curves of cell viability were obtained for each of the seven cell lines upon exposure to Co-Fe NPs. Increase of oxidative stress was induced by Co-Fe NPs and found to be dependent on the cell type. A high linear correlation (R 2 =0.97) was found between the toxicity of Co-Fe NPs and the extent of ROS generation following their exposure to Co-Fe NPs. The algorithm we applied to model the observed toxicity belongs to a type of supervised classifier. The decision tree model yielded the following order with decrease of the ranking parameter: NP concentrations (as the most influencing parameter), cell type (possessing the following hierarchy of cell sensitivity towards viability decrease: TK6 > Lung slices > NCIH441 > Caco-2 = MDCK > A549 > HepG2 = Dendritic) and time of exposure, where the highest-ranking parameter (NP concentration) provides the highest information gain with respect to toxicity. The validity of the chosen decision tree model J48 was established by yielding a higher accuracy than that of the well-known “naive bayes” classifier. Conclusions The observed correlation between the oxidative stress, caused by the presence of the Co-Fe NPs, with the hierarchy of sensitivity of the different cell types towards toxicity, suggests that oxidative stress is one possible mechanism for the toxicity of Co-Fe NPs.

Data mining tools can be very beneficial for discovering interesting and useful patterns in complicated manufacturing processes. These patterns can be used, for example, to improve manufacturing quality. However, data accumulated in manufacturing plants have unique characteristics, such as unbalanced distribution of the target attribute, and a small training set relative to the number of input features. Thus, conventional methods are inaccurate in quality improvement cases. Recent research shows, however, that a decomposition tactic may be appropriate here and this paper presents a new feature set decomposition methodology that is capable of dealing with the data characteristics associated with quality improvement. In order to examine the idea, a new algorithm called (Breadth-Oblivious-Wrapper) BOW has been developed. This algorithm performs a breadth first search while using a new F-measure splitting criterion for multiple oblivious trees. The new algorithm was tested on various real-world manufacturing datasets, specifically the food processing industry and integrated circuit fabrication. The obtained results have been compared to other methods, indicating the superiority of the proposed methodology. [PUBLICATION ABSTRACT]