Data Mining Practical Machine Learning Tools and Techniques

A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented.

Multiobjective evolutionary algorithms: A survey of the state of the art

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.

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A Survey on Evolutionary Computation Approaches to Feature Selection

Objective  To provide an overview of prediction models for risk of cardiovascular disease (CVD) in the general population. Design  Systematic review. Data sources  Medline and Embase until June 2013. Eligibility criteria for study selection  Studies describing the development or external validation of a multivariable model for predicting CVD risk in the general population. Results  9965 references were screened, of which 212 articles were included in the review, describing the development of 363 prediction models and 473 external validations. Most models were developed in Europe (n=167, 46%), predicted risk of fatal or non-fatal coronary heart disease (n=118, 33%) over a 10 year period (n=209, 58%). The most common predictors were smoking (n=325, 90%) and age (n=321, 88%), and most models were sex specific (n=250, 69%). Substantial heterogeneity in predictor and outcome definitions was observed between models, and important clinical and methodological information were often missing. The prediction horizon was not specified for 49 models (13%), and for 92 (25%) crucial information was missing to enable the model to be used for individual risk prediction. Only 132 developed models (36%) were externally validated and only 70 (19%) by independent investigators. Model performance was heterogeneous and measures such as discrimination and calibration were reported for only 65% and 58% of the external validations, respectively. Conclusions  There is an excess of models predicting incident CVD in the general population. The usefulness of most of the models remains unclear owing to methodological shortcomings, incomplete presentation, and lack of external validation and model impact studies. Rather than developing yet another similar CVD risk prediction model, in this era of large datasets, future research should focus on externally validating and comparing head-to-head promising CVD risk models that already exist, on tailoring or even combining these models to local settings, and investigating whether these models can be extended by addition of new predictors.

https://www.bmj.com/content/bmj/353/bmj.i2416.full.pdf

Prediction models for cardiovascular disease risk in the general population: systematic review

Previous research has resulted in a number of different algorithms for rule discovery. Two approaches discussed here, the ‘all-rules’ algorithm and multi-objective metaheuristics, both result in the production of a large number of partial classification rules, or ‘nuggets’, for describing different subsets of the records in the class of interest. This paper describes the application of a number of different clustering algorithms to these rules, in order to identify similar rules and to better understand the data.

Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms

Feature selection plays an important role in the data mining process. It is needed to deal with the excessive number of features, which can become a computational burden on the learning algorithms. It is also necessary, even when computational resources are not scarce, since it improves the accuracy of the machine learning tasks, as we will see in the upcoming sections. In this review, we discuss the different feature selection approaches, and the relation between them and the various machine learning algorithms.

Survey on Feature Selection

A scoping review of the use of Twitter for public health research.

Feature subset selection (FSS) has received a great deal of attention in statistics, machine learning, and data mining. Real world data analyzed by data mining algorithms can involve a large number of redundant or irrelevant features or simply too many features for a learning algorithm to handle them efficiently. Feature selection is becoming essential as databases grow in size and complexity. The selection process is expected to bring benefits in terms of better performing models, computational efficiency, and simpler more understandable models. Evolutionary computation (EC) encompasses a number of naturally inspired techniques such as genetic algorithms, genetic programming, ant colony optimization, or particle swarm optimization algorithms. Such techniques are well suited to feature selection because the representation of a feature subset is straightforward and the evaluation can also be easily accomplished through the use of wrapper or filter algorithms. Furthermore, the capability of such heuristic algorithms to efficiently search large search spaces is of great advantage to the feature selection problem. Here, we review the use of different EC paradigms for feature selection in classification problems. We discuss details of each implementation including representation, evaluation, and validation. The review enables us to uncover the best EC algorithms for FSS and to point at future research directions. WIREs Data Mining Knowl Discov 2013, 3:381–407. doi: 10.1002/widm.1106



Conflict of interest: The authors have declared no conflicts of interest for this article.



For further resources related to this article, please visit the WIREs website.

Evolutionary computation for feature selection in classification problems

In this paper, we experiment with a combination of innovative approaches to rule induction to encourage the production of interesting sets of classification rules. These include multi-objective metaheuristics to induce the rules; measures of rule dissimilarity to encourage the production of dissimilar rules; and rule clustering algorithms to evaluate the results obtained.

Our previous implementation of NSGA-II for rule induction produces a set of cc-optimal rules (coverage-confidence optimal rules). Among the set of rules produced there may be rules that are very similar. We explore the concept of rule similarity and experiment with a number of modifications of the crowding distance to increasing the diversity of the partial classification rules produced by the multi-objective algorithm.

Developments on a multi-objective metaheuristic (MOMH) algorithm for finding interesting sets of classification rules

Objective To evaluate the performance of ASSIGN against the Framingham equations for predicting 10 year risk of cardiovascular disease in a UK cohort of patients from general practice and to make the evaluation comparable to an independent evaluation of QRISK on the same cohort. Design Prospective open cohort study. Setting 288 practices from England and Wales contributing to The Health Improvement Network (THIN) database. Participants Patients registered with 288 UK practices for some period between January 1995 and March 2006. The number of records available was 1 787 169. Main outcome measures First diagnosis of myocardial infarction, coronary heart disease, stroke and transient ischaemic attacks recorded. Methods We implemented the Anderson Framingham Coronary Heart Disease and Stroke models, ASSIGN, and a more recent Framingham Cox proportional-hazards model and analysed their calibration and discrimination. Results Calibration showed that all models tested over-estimated risk particularly for men. ASSIGN showed better discrimination with higher AUROC (0.756/0.792 for men/women), D statistic (1.35/1.58 for men/women), and R 2 (30.47%/37.39% for men/women). The performance of ASSIGN was comparable to that of QRISK on the same cohort. Models agreed on 93–97% of categorical (high/lower) risk assessments and when they disagreed, ASSIGN was often closer to the estimated Kaplan-Meier incidence. ASSIGN also provided a steeper gradient of deprivation and discriminated between those with and without recorded family history of CVD. The estimated incidence was twice/three times as high for women/men with a recorded family history of CVD. Conclusions For systematic CVD risk assessment all models could usefully be applied, but ASSIGN improved on the gradient of deprivation and accounted for recorded family history whereas the Framingham equations did not. However, all models display relatively low specificity and sensitivity. An additional conclusion is that the recording of family history of CVD in primary care databases needs to improve given its importance in risk assessment.

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Beatriz de la Iglesia

Papers

Survey on Feature Selection

A scoping review of the use of Twitter for public health research.

Evolutionary computation for feature selection in classification problems

Developments on a multi-objective metaheuristic (MOMH) algorithm for finding interesting sets of classification rules

Performance of the ASSIGN cardiovascular disease risk score on a UK cohort of patients from general practice