A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree

Malaria larvae accept explosive variable lifecycle as they spread across numerous mosquito vector stratosphere. Transcriptomes arise in thousands of diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene expression that has led to enhanced understanding of genetic queries. RNA-seq tests transcript of gene expression, and provides methodological enhancements to machine learning procedures. Researchers have proposed several methods in evaluating and learning biological data. Genetic algorithm (GA) as a feature selection process is used in this study to fetch relevant information from the RNA-Seq Mosquito Anopheles gambiae malaria vector dataset, and evaluates the results using kth nearest neighbor (KNN) and decision tree classification algorithms. The experimental results obtained a classification accuracy of 88.3 and 98.3 percents respectively

An analysis of ensemble pruning techniques based on ordered aggregation

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. G. Martínez-Muñoz, D. Hernández-Lobato and A. Suárez, "An analysis of ensemble pruning techniques based on ordered aggregation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 245-249, February 2009Several pruning strategies that can be used to reduce the size and increase the accuracy of bagging ensembles are analyzed. These heuristics select subsets of complementary classifiers that, when combined, can perform better than the whole ensemble. The pruning methods investigated are based on modifying the order of aggregation of classifiers in the ensemble. In the original bagging algorithm, the order of aggregation is left unspecified. When this order is random, the generalization error typically decreases as the number of classifiers in the ensemble increases. If an appropriate ordering for the aggregation process is devised, the generalization error reaches a minimum at intermediate numbers of classifiers. This minimum lies below the asymptotic error of bagging. Pruned ensembles are obtained by retaining a fraction of the classifiers in the ordered ensemble. The performance of these pruned ensembles is evaluated in several benchmark classification tasks under different training conditions. The results of this empirical investigation show that ordered aggregation can be used for the efficient generation of pruned ensembles that are competitive, in terms of performance and robustness of classification, with computationally more costly methods that directly select optimal or near-optimal subensembles.The authors acknowledge support form the Spanish Ministerio de Educación y Ciencia under Project TIN2007-66862-C02-0

Machine Learning Approach for Credit Score Predictions

This paper addresses the problem of managing the significant rise in requests for credit products that banking and financial institutions face. The aim is to propose an adaptive, dynamic heterogeneous ensemble credit model that integrates the XGBoost and Support Vector Machine models to improve the accuracy and reliability of risk assessment credit scoring models. The method employs machine learning techniques to recognise patterns and trends from past data to anticipate future occurrences. The proposed approach is compared with existing credit score models to validate its efficacy using five popular evaluation metrics, Accuracy, ROC AUC, Precision, Recall and F1_Score. The paper highlights credit scoring models’ challenges, such as class imbalance, verification latency and concept drift. The results show that the proposed approach outperforms the existing models regarding the evaluation metrics, achieving a balance between predictive accuracy and computational cost. The conclusion emphasises the significance of the proposed approach for the banking and financial sector in developing robust and reliable credit scoring models to evaluate the creditworthiness of their clients

Improved credit scoring model using XGBoost with Bayesian hyper-parameter optimization

Several credit-scoring models have been developed using ensemble classifiers in order to improve the accuracy of assessment. However, among the ensemble models, little consideration has been focused on the hyper-parameters tuning of base learners, although these are crucial to constructing ensemble models. This study proposes an improved credit scoring model based on the extreme gradient boosting (XGB) classifier using Bayesian hyper-parameters optimization (XGB-BO). The model comprises two steps. Firstly, data pre-processing is utilized to handle missing values and scale the data. Secondly, Bayesian hyper-parameter optimization is applied to tune the hyper-parameters of the XGB classifier and used to train the model. The model is evaluated on four widely public datasets, i.e., the German, Australia, lending club, and Polish datasets. Several state-of-the-art classification algorithms are implemented for predictive comparison with the proposed method. The results of the proposed model showed promising results, with an improvement in accuracy of 4.10%, 3.03%, and 2.76% on the German, lending club, and Australian datasets, respectively. The proposed model outperformed commonly used techniques, e.g., decision tree, support vector machine, neural network, logistic regression, random forest, and bagging, according to the evaluation results. The experimental results confirmed that the XGB-BO model is suitable for assessing the creditworthiness of applicants

Modeling Movement Disorders in Parkinson's Disease using Computational Intelligence

Parkinson's is the second most common neurodegenerative disease after Alzheimer's Disease and affects 127,000 people in the UK alone. Providing the most appropriate treatment pathway can prove challenging owing to the difficulty in obtaining an accurate diagnosis; due to its similarity in symptoms with other neurodegenerative diseases, it is estimated that in the United Kingdom around 24% of cases are misdiagnosed by general neurologists. A means of providing an accurate and early diagnosis of Parkinson's Disease would thereby enable a more effective management of the disease, increased quality of life for patients, and reduce costs to the healthcare system. The work described in this thesis details progress towards this goal by modeling movement disorders in the form of positional data recorded from simple movement tasks, building towards a fully objective diagnostic system without requiring any specialist domain knowledge. This is accomplished by modeling established movement disorder markers using Evolutionary Algorithms to train ensembles, before implementing feature design strategies with both Genetic Programming and Echo State Networks. The findings of this study make an important contribution to the area of data mining, including: the demonstration that Computational Intelligence-based feature design strategies can be competitive to conventional models using features extracted with expert domain knowledge; a thorough survey of evolutionary ensemble research; and the development of a novel evolutionary ensemble approach comprising traditional single objective Evolutionary Algorithm. Furthermore, an extension to a Genetic Programming feature design strategy for periodic time series is detailed, in addition to demonstrating that Echo State Networks can be directly applied to time series classification as a feature design method. This research was carried out in the context of building an applied diagnostic aid and required developing models with means of indicating the most discriminatory aspects of the sequence data, thereby facilitating inference of the precise mechanics of movement disorders to clinical neurologists

Static and dynamic overproduction and selection of classifier ensembles with genetic algorithms

The overproduce-and-choose sttategy is a static classifier ensemble selection approach, which is divided into overproduction and selection phases. This thesis focuses on the selection phase, which is the challenge in overproduce-and-choose strategy. When this phase is implemented as an optimization process, the search criterion and the search algorithm are the two major topics involved. In this thesis, we concentrate in optimization processes conducted using genetic algorithms guided by both single- and multi-objective functions. We first focus on finding the best search criterion. Various search criteria are investigated, such as diversity, the error rate and ensemble size. Error rate and diversity measures are directly compared in the single-objective optimization approach. Diversity measures are combined with the error rate and with ensemble size, in pairs of objective functions, to guide the multi-optimization approach. Experimental results are presented and discussed. Thereafter, we show that besides focusing on the characteristics of the decision profiles of ensemble members, the control of overfitting at the selection phase of overproduce-and-choose strategy must also be taken into account. We show how overfitting can be detected at the selection phase and present three strategies to control overfitting. These strategies are tailored for the classifier ensemble selection problcm and compared. This comparison allows us to show that a global validation strategy should be applied to control overfitting in optimization processes involving a classifier ensembles selection task. Furthermore, this study has helped us establish that this global validation strategy can be used as a tool to measure the relationship between diversity and classification performance when diversity measures are employed as single-objective functions. Finally, the main contribution of this thesis is a proposed dynamic overproduce-and-choose strategy. While the static overproduce-and-choose selection strategy has traditionally focused on finding the most accurate subset of classifiers during the selection phase, and using it to predict the class of all the test samples, our dynamic overproduce-and- choose strategy allows the selection of the most confident subset of classifiers to label each test sample individually. Our method combines optimization and dynamic selection in a two-level selection phase. The optimization level is intended to generate a population of highly accurate classifier ensembles, while the dynamic selection level applies measures of confidence in order to select the ensemble with the highest degree of confidence in the current decision. Three different confidence measures are presented and compared. Our method outperforms classical static and dynamic selection strategies