[Tom Mitchell Machine Learning Solution Manual.zip
Hanne Rylaarsdam
A fully automated, computer-controlled test stand capable of rapidly creating and electrochemically characterizing any arbitrary liquid electrolyte solution is described. Hundreds of different electrolytes were studied, and the results were used to verify the precision and accuracy of the system. To test the functionality of the approach, several 2-dimensional co-solvated electrolyte solutions containing blends of aqueous sulfates and nitrates were rapidly created and examined automatically. The test stand took less than a day to conduct these searches, while conventional manual methods would have taken much longer. The demonstrated standard error of the test-stand was 0.5 mS/cm on conductivity and 0.02 V for voltage stability window measurements, and several of the combinations studied revealing surprisingly high voltage stability and conductivity values. The demonstrated success of the test-stand in a 2-dimensional search spaces shows the promise of conducting high speed co-optimization studies of liquid electrolytes in particular when used in concert with a machine learning-based real time/in-loop data assessment computational package.
Today, machine learning (ML) is the fastest growing technical field, at the intersection of informatics and statistics, tightly connected with data science and knowledge discovery, and health is among the greatest challenges [2, 3].
Recent progress in ML has been driven both by the development of new learning algorithms and theory and by the ongoing explosion of data and, at the same time, low-cost computation. The adoption of data-intensive ML-algorithms can be found in all application areas of health informatics, and is particularly useful for brain informatics, ranging from basic research to understand intelligence [8] to a wide range of specific brain informatics research [9]. The application of ML methods in biomedicine and health can, for instance, lead to more evidence-based decision-making and helping to go toward personalized medicine [10].
Most colleagues from the ML community are concentrating on automatic machine learning (aML), with the grand goal of bringing humans-out-of-the-loop, and a best practice real-world example can be found in autonomous vehicles [14].
This article is a brief introduction to iML, discussing some challenges and benefits of this approach for health informatics. It starts by motivating the need of a human-in-the-learning-loop and discusses three potential application examples of iML, followed by a very brief overview on the roots of iML in historical sequence: reinforcement learning (1950), preference learning (1987), and active learning (1996). The overview concludes with discussing three examples of potential future research challenges, relevant for solving problems in the health informatics domain: multi-task learning, transfer learning, and multi-agent hybrid systems. The article concludes with emphasizing that successful future research in ML for health informatics, as well as the successful application of ML for solving health informatics problems needs a concerted effort, fostering integrative research between experts ranging from disciplines such as data science to visual analytics. Tackling such complex research undertakings needs both disciplinary excellence and cross-disciplinary networking without boundaries.
Scenario D now shows the iML-approach, where the human expert is seen as an agent directly involved in the actual learning phase, step-by-step influencing measures such as distance, cost functions, etc.
Obvious concerns may emerge immediately and one can argue: what about the robustness of this approach, the subjectivity, the transfer of the (human) agents; many questions remain open and are subject for future research, particularly in evaluation, replicability, robustness, etc.
There is evidence that humans sometimes still outperform ML-algorithms, e.g., in the instinctive, often almost instantaneous interpretation of complex patterns, for example, in diagnostic radiologic imaging: A promising technique to fill the semantic gap is to adopt an expert-in-the-loop approach, to integrate the physicians high-level expert knowledge into the retrieval process by acquiring his/her relevance judgments regarding a set of initial retrieval results [22].
Grouping data sets into clusters based on their similarity is of enormous importance, and the similarity measure is the key aspect of the clustering process. Clustering is usually studied in unsupervised learning settings, but there is a huge problem with real-world data, because such data rarely result from the so-called well-behaved probabilistic models. Consequently, the study of interactive clustering algorithms is a growing area of research: Awasthi et al. [33] studied the problem of designing local algorithms for interactive clustering and proposed an interactive model and provided strong experimental evidence supporting the practical applicability of it. Their model starts with an initial clustering of the data, then the user can directly interact with the algorithm step-wise. In each step, the user provides limited feedback on the current clustering in the form of split-and-merge requests. The algorithm then makes a local edit to the clustering that is consistent with the user feedback. Such edits are aimed at improving the problematic part of the clustering pointed out by the human-in-the-loop. The goal of the algorithm is to quickly converge (using as few requests as possible) to a clustering that the user is happy with, which is called target clustering. More theoretical foundations of clustering with interactive feedback can be found in [34].
In protein structure prediction, there is still much interest in using amino acid interaction preferences to align (thread) a protein sequence to a known structural motif. The protein alignment decision problem (does there exist an alignment (threading) with a score less than or equal to K?) is NP-complete, and the related problem of finding the globally optimal protein threading is NP-hard. Therefore, no polynomial time algorithm is possible (unless P = NP). Consequently, the protein folding problem is NP-complete [35]. Health informatics is faced with many problems that (still) require the human-in-the-loop, e.g., genome annotation, image analysis, knowledge-base population, and protein structure. In some cases, humans are needed in vast quantities (e.g., in cancer research), whereas in others, we need just a few very specialized experts in certain fields (e.g., in the case of rare diseases). Crowdsourcing encompasses an emerging collection of approaches for harnessing such distributed human intelligence. Recently, the bioinformatics community has begun to apply crowdsourcing in a variety of contexts, yet few resources are available that describe how these human-powered systems work and how to use them effectively in scientific domains. Generally, there are large-volume micro-tasks and highly difficult mega-tasks [36]. A good example of such an approach is foldit, an experimental game which takes advantage of crowdsourcing for category discovery of new protein structures [37]. Crowdsourcing and collective intelligence (putting many experts-into-the-loop) would generally offer much potential to foster translational medicine (bridging biomedical sciences and clinical applications) by providing platforms upon which interdisciplinary workforces can communicate and collaborate [38].
Privacy preserving machine learning is an important issue, fostered by anonymization, in which a record is released only if it is indistinguishable from k other entities in the data. k-anonymity is highly dependent on spatial locality in order to effectively implement the technique in a statistically robust way, and in high dimensionalities data become sparse, hence, the concept of spatial locality is not easy to define. Consequently, it becomes difficult to anonymize the data without an unacceptably high amount of information loss [39]. Consequently, the problem of k-anonymization is on the one hand NP-hard, on the other hand the quality of the result obtained can be measured at the given factors: k-anonymity means that attributes are suppressed or generalized until each row in a database is identical with at least \(k-1\) other rows [40, 41]; l-diversity as extension of the k-anonymity model reduces the granularity of data representation by generalization and suppression so that any given record maps onto at least k other records in the data [42]; t-closeness is a refinement of l-diversity by reducing the granularity of a data representation, and treating the values of an attribute distinctly by taking into account the distribution of data values for that attribute [43]; and delta-presence, which links the quality of anonymization to the risk posed by inadequate anonymization [44]), but not with regard to the actual security of the data, i.e., the re-identification through an attacker. For this purpose, certain assumptions about the background knowledge of the hypothetical enemy must be made. With regard to the particular demographic and cultural clinical environment this is best done by a human agent. Thus, the problem of (k-)anonymization represents a natural application domain for iML.
Particularly in the patient admission, human agents have the advantage to perceive the total situation at a glance. This aptitude results from the ability of transfer learning, where knowledge can be transferred from one situation to another situation, in which model parameters, i.e., learned features or contextual knowledge are transferred.
Reinforcement learning (RL) was discussed by Turing [45] and is to date the most studied approach in ML. The theory behind RL is rooted in neuropsychological issues on behavior of how agents may optimize their control of an complex environment. Consequently, RL is a branch of ML concerned with using experience gained through interacting with the world and evaluative feedback to improve the ability of a system to generate behavioral decisions. This has been called the artificial intelligence problem in a microcosm because learning agents must act autonomously to perform well and to achieve their goals. Driven by the increasing availability of rich data, RL has achieved great results, including developments in fundamental ML-relevant areas, such as generalization, planning, exploration, and empirical methodology, leading to better applicability to real-world problems [46].
795a8134c1