Constructing Complexity-efficient Features in XCS with Tree-based Rule Conditions

A major goal of machine learning is to create techniques that abstract away irrelevant information. The generalisation property of standard Learning Classifier System (LCS) removes such information at the feature level but not at the feature interaction level. Code Fragments (CFs), a form of tree-based programs, introduced feature manipulation to discover important interactions, but they often contain irrelevant information, which causes structural inefficiency. XOF is a recently introduced LCS that uses CFs to encode building blocks of knowledge about feature interaction. This paper aims to optimise the structural efficiency of CFs in XOF. We propose two measures to improve constructing CFs to achieve this goal. Firstly, a new CF-fitness update estimates the applicability of CFs that also considers the structural complexity. The second measure we can use is a niche-based method of generating CFs. These approaches were tested on Even-parity and Hierarchical problems, which require highly complex combinations of input features to capture the data patterns. The results show that the proposed methods significantly increase the structural efficiency of CFs, which is estimated by the rule "generality rate". This results in faster learning performance in the Hierarchical Majority-on problem. Furthermore, a user-set depth limit for CF generation is not needed as the learning agent will not adopt higher-level CFs once optimal CFs are constructed

Trajectory Tracking Control of Skid-Steering Mobile Robots with Slip and Skid Compensation using Sliding-Mode Control and Deep Learning

Slip and skid compensation is crucial for mobile robots' navigation in outdoor environments and uneven terrains. In addition to the general slipping and skidding hazards for mobile robots in outdoor environments, slip and skid cause uncertainty for the trajectory tracking system and put the validity of stability analysis at risk. Despite research in this field, having a real-world feasible online slip and skid compensation is still challenging due to the complexity of wheel-terrain interaction in outdoor environments. This paper presents a novel trajectory tracking technique with real-world feasible online slip and skid compensation at the vehicle-level for skid-steering mobile robots in outdoor environments. The sliding mode control technique is utilized to design a robust trajectory tracking system to be able to consider the parameter uncertainty of this type of robot. Two previously developed deep learning models [1], [2] are integrated into the control feedback loop to estimate the robot's slipping and undesired skidding and feed the compensator in a real-time manner. The main advantages of the proposed technique are (1) considering two slip-related parameters rather than the conventional three slip parameters at the wheel-level, and (2) having an online real-world feasible slip and skid compensator to be able to reduce the tracking errors in unforeseen environments. The experimental results show that the proposed controller with the slip and skid compensator improves the performance of the trajectory tracking system by more than 27%

New Fitness Functions in Binary Particle Swarm Optimisation for Feature Selection

Abstract-Feature selection is an important data preprocessing technique in classification problems. This paper proposes two new fitness functions in binary particle swarm optimisation (BPSO) for feature selection to choose a small number of features and achieve high classification accuracy. In the first fitness function, the relative importance of classification performance and the number of features are balanced by using a linearly increasing weight in the evolutionary process. The second is a two-stage fitness function, where classification performance is optimised in the first stage and the number of features is taken into account in the second stage. K-nearest neighbour (KNN) is employed to evaluate the classification performance in the experiments on ten datasets. Experimental results show that by using either of the two proposed fitness functions in the training process, in almost all cases, BPSO can select a smaller number of features and achieve higher classification accuracy on the test sets than using overall classification performance as the fitness function. They outperform two conventional feature selection methods in almost all cases. In most cases, BPSO with the second fitness function can achieve better performance than with the first fitness function in terms of classification accuracy and the number of features

A Survey on Evolutionary Computation Approaches to Feature Selection

Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.</p

Provider diversity in the English NHS: a study of recent developments in four local health economies

Objectives: The overall objective of the research was to assess the impact of provider diversity on quality and innovation in the English NHS. The aims were to map the extent of diverse provider activity, identify the differences in performance between Third Sector Organisations (TSOs), for-profit private enterprises, and incumbent organisations within the NHS, and the factors that affect the entry and growth of new private and TSOs. Methods: Case studies of four Local Health Economies (LHEs). Data included: semi-structured interviews with 48 managerial and clinical staff from NHS organizations and providers from the private and Third Sector; some documentary evidence; a focus group with service users; and routine data from the Care Quality Commission and Companies House. Data collection was mainly between November 2008 and November 2009. Results: Involvement of diverse providers in the NHS is limited. Commissioners’ local strategies influence degrees of diversity. Barriers to the entry for TSOs include lack of economies of scale in the bidding process. Private providers have greater concern to improve patient pathways and patient experience, whereas TSOs deliver quality improvements by using a more holistic approach and a greater degree of community involvement. Entry of new providers drives NHS Trusts to respond by making improvements. Information sharing diminishes as competition intensifies. Conclusions: There is scope to increase the participation of diverse providers in the NHS, but care must be taken not to damage public accountability, overall productivity, equity and NHS providers (especially acute hospitals, which are likely to remain in the NHS) in the process

Design for ground beetle abundance and diversity sampling within the National Ecological Observatory Network

The National Ecological Observatory Network (NEON) will monitor ground beetle populations across a network of broadly distributed sites because beetles are prevalent in food webs, are sensitive to abiotic factors, and have an established role as indicator species of habitat and climatic shifts. We describe the design of ground beetle population sampling in the context of NEON's long-term, continentalscale monitoring program, emphasizing the sampling design, priorities, and collection methods. Freely available NEON ground beetle data and associated field and laboratory samples will increase scientific understanding of how biological communities are responding to land-use and climate change.Peer reviewe

Protocol for a national cohort study to explore the long-term clinical and patient-reported outcomes and cost-effectiveness of implant-based and autologous breast reconstruction after mastectomy for breast cancer: The Brighter Study

Introduction Breast reconstruction (BR) is offered to improve quality of life for women with breast cancer undergoing mastectomy. As most women will be long-Term breast cancer survivors, high-quality information regarding the long-Term outcomes of different BR procedures is essential to support informed decision-making. As different techniques vary considerably in cost, policymakers also require high-quality cost-effectiveness evidence to inform care. The Brighter study aims to explore the long-Term clinical and patient-reported outcomes (PROs) of implant-based and autologous BR and use health economic modelling to compare the long-Term cost-effectiveness of different reconstructive techniques. Methods and analysis Women undergoing mastectomy and/or BR following a diagnosis of breast cancer between 1 January 2008 and 31 March 2009 will be identified from hospital episode statistics (HES). Surviving women will be contacted and invited to complete validated PRO measures including the BREAST-Q, EQ-5D-5L and ICECAP-A, or opt out of having their data included in the HES analysis. Long-Term clinical outcomes will be explored using HES data. The primary outcome will be rates of revisional surgery between implant-based and autologous procedures. Secondary outcomes will include rates of secondary reconstruction and reconstruction failure. The long-Term PROs of implant-based and autologous reconstruction will be compared using BREAST-Q, EQ-5D-5L and ICECAP-A scores. Multivariable regression will be used to examine the relationship between long-Term outcomes, patient comorbidities, sociodemographic and treatment factors. A Markov model will be developed using HES and PRO data and published literature to compare the relative long-Term cost-effectiveness of implant-based and autologous BR. Ethics and dissemination The Brighter study has been approved by the South-West-Central Bristol Research Ethics Committee (20/SW/0020), and the Confidentiality Advisory Group (20/CAG/0021). Results will be published in peer-reviewed journals and presented at national meetings. We will work with the professional associations, charities and patient groups to disseminate the results

Impact of procedure type on revisional surgery and secondary reconstruction after immediate breast reconstruction in a population-based cohort

Women considering immediate breast reconstruction require high-quality information about the likely need for secondary reconstruction and the long-term risk of revisional surgery to make fully informed decisions about different reconstructive options. Such data are currently lacking. This study aimed to explore the impact of reconstruction type on the number of revisions and secondary reconstructions performed 3, 5, and 8 years after immediate breast reconstruction in a large population-based cohort. Women undergoing unilateral mastectomy and immediate breast reconstruction for breast cancer or ductal carcinoma in situ in England between 1 April 2009 and 31 March 2015 were identified from National Health Service Hospital Episode Statistics. Numbers of revisions and secondary reconstructions in women undergoing primary definitive immediate breast reconstruction were compared by procedure type at 3, 5, and 8 years after index surgery. Some 16 897 women underwent immediate breast reconstruction with at least 3 years' follow-up. Of these, 14 069 had a definitive reconstruction with an implant only (5193), latissimus dorsi flap with (3110) or without (2373) an implant, or abdominal free flap (3393). Women undergoing implant-only reconstruction were more likely to require revision, with 69.5 per cent (747 of 1075) undergoing at least one revision by 8 years compared with 49.3 per cent (1568 of 3180) in other reconstruction groups. They were also more likely to undergo secondary reconstruction, with the proportion of women having further reconstructive procedures increasing over time: 12.8 per cent (663 of 5193) at 3 years, 14.3 per cent (535 of 3752) at 5 years, and 17.6 per cent (189 of 1075) at 8 years. Long-term rates of revisions and secondary reconstructions were considerably higher after primary implant-based reconstruction than autologous procedures. These results should be shared with patients to support informed decision-making. [Abstract copyright: © The Author(s) 2023. Published by Oxford University Press on behalf of BJS Society Ltd.