Heuristic target class selection for advancing performance of coverage-based rule learning

Rule learning is a popular branch of machine learning, which can provide accurate and interpretable classification results. In general, two main strategies of rule learning are referred to as 'divide and conquer' and 'separate and con-quer'. Decision tree generation that follows the former strategy has a serious drawback, which is known as the replicated sub-tree problem, resulting from the constraint that all branches of a decision tree must have one or more common attributes. The above problem is likely to result in high computational complexity and the risk of overfitting, which leads to the necessity to develop rule learning algorithms (e.g., Prism) that follow the separate and conquer strategy. The replicated sub-tree problem can be effectively solved using the Prism algorithm , but the trained models are still complex due to the need of training an independent rule set for each selected target class. In order to reduce the risk of overfitting and the model complexity, we propose in this paper a variant of the Prism algorithm referred to as PrismCTC. The experimental results show that the PrismCTC algorithm leads to advances in classification performance and reduction of model complexity, in comparison with the C4.5 and Prism algorithms

Behavioral Economics and Health Economics

The health sector is filled with institutions and decision-making circumstances that create friction in markets and cognitive errors by decision makers. This paper examines the potential contributions to health economics of the ideas of behavioral economics. The discussion presented here focuses on the economics of doctor-patient interactions and some aspects of quality of care. It also touches on issues related to insurance and the demand for health care. The paper argues that long standing research impasses may be aided by applying concepts from behavioral economics.

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included

Multi-stage mixed rule learning approach for advancing performance of rule-based classification

Rule learning is a special type of machine learning approaches, and its key advantage is the generation of interpretable models, which provides a transparent process of showing how an input is mapped to an output. Traditional rule learning algorithms are typically based on Boolean logic for inducing rule antecedents, which are very effective for training models on data sets that involve discrete attributes only. When continuous attributes are present in a data set, traditional rule learning approaches need to employ crisp intervals. However, in reality, problems usually show shades of grey, which motivated the development of fuzzy rule learning approaches by employing fuzzy intervals for handling continuous attributes. While a data set contains a large portion of discrete attributes or even no continuous attributes, fuzzy approaches cannot be used to learn rules effectively, leading to a drop in the performance. In this paper, a multi-stage approach of mixed rule learning is proposed, which involves strategic combination of both traditional and fuzzy approaches to handle effectively various types of attributes. We compare our proposed approach with existing algorithms of rule learning. Our experimental results show that our proposed approach leads to significant advances in the performance compared with the existing algorithms

Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides

Background Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decision making processes leading to irrational outcomes is a major concern for their implementation. With the rising antibiotic resistance, antimicrobial peptides (AMPs) have increasingly gained attention as novel therapeutic agents. This challenging design problem requires peptides which meet the multiple constraints of limiting drug-resistance in bacteria, preventing secondary infections from imbalanced microbial flora, and avoiding immune system suppression. AMPs offer a promising, bioinspired design space to targeting antimicrobial activity, but their versatility also requires the curated selection from a combinatorial sequence space. This space is too large for brute-force methods or currently known rational design approaches outside of machine learning. While there has been progress in using the design space to more effectively target AMP activity, a widely applicable approach has been elusive. The lack of transparency in machine learning has limited the advancement of scientific knowledge of how AMPs are related among each other, and the lack of general applicability for fully rational approaches has limited a broader understanding of the design space. Methods Here we combined an evolutionary method with rough set theory, a transparent machine learning approach, for designing antimicrobial peptides (AMPs). Our method achieves the customization of AMPs using supervised learning boundaries. Our system employs in vitro bacterial assays to measure fitness, codon-representation of peptides to gain flexibility of sequence selection in DNA-space with a genetic algorithm and machine learning to further accelerate the process. Results We use supervised machine learning and a genetic algorithm to find a peptide active against S. epidermidis, a common bacterial strain for implant infections, with an improved aggregation propensity average for an improved ease of synthesis. Conclusions Our results demonstrate that AMP design can be customized to maintain activity and simplify production. To our knowledge, this is the first time when codon-based genetic algorithms combined with rough set theory methods is used for computational search on peptide sequences

Heuristic creation of deep rule ensemble through iterative expansion of feature space

Rule learning approaches, which essentially aim to gerenate a decision tree or a set of “if-then” rules, have been popularly used in practice for automatically building rule-based models for prediction tasks, e.g., classification and regression. The key strength of rule-based models is their ability to interpret how an output is obtained given an input, in comparison with models trained by other machine learning approaches, e.g., neural networks. Moreover, ensemble learning approaches have been adopted as a popular way for advancing the performance of rule-based prediction through producing multiple rule-based models with diversity. Traditional approaches of ensemble learning are typically designed to train a single ensemble. In recent years, there have been some studies on creation of multiple ensembles towards increasing the diversity among rule-based models and the depth of ensemble learning. In this paper, we propose a feature expansion driven approach for automatic creation of deep rule ensembles, i.e., the dimensionality of the feature space is increased at each iteration by adding features newly created at the previous iteration. The proposed approach is compared with more recent approaches of rule learning and ensemble creation. The experimental results show that the proposed approach achieves improved performance on various data sets