Comparing Methods to Extract the Knowledge from Neural Networks

Neural networks (NN) have been shown to be accurate classifiers in many domains. Unfortunately, the lack of NN’s explanatory capability of knowledge learned has somewhat limited their application. A stream of research has therefore developed focusing on knowledge extraction from within neural networks. The literature, unfortunately, lacks consensus on how best to extract knowledge from help neural networks. Additionally, there is a lack of empirical studies that compare existing algorithms on relevant performance measures. Therefore, this study attempts to help fill this gap by comparing two different approaches to extracting IF-THEN rules from feedforward NN. The results show a significant difference in the performance of the two algorithms depending on the structure of the dataset utilized

Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts

There is recent interest in using model hubs, a collection of pre-trained models, in computer vision tasks. To utilize the model hub, we first select a source model and then adapt the model for the target to compensate for differences. While there is yet limited research on model selection and adaption for computer vision tasks, this holds even more for the field of renewable power. At the same time, it is a crucial challenge to provide forecasts for the increasing demand for power forecasts based on weather features from a numerical weather prediction. We close these gaps by conducting the first thorough experiment for model selection and adaptation for transfer learning in renewable power forecast, adopting recent results from the field of computer vision on 667 wind and photovoltaic parks. To the best of our knowledge, this makes it the most extensive study for transfer learning in renewable power forecasts reducing the computational effort and improving the forecast error. Therefore, we adopt source models based on target data from different seasons and limit the amount of training data. As an extension of the current state of the art, we utilize a Bayesian linear regression for forecasting the response based on features extracted from a neural network. This approach outperforms the baseline with only seven days of training data. We further show how combining multiple models through ensembles can significantly improve the model selection and adaptation approach

A Quantitative Evaluation of Global, Rule-Based Explanations of Post-Hoc, Model Agnostic Methods

Understanding the inferences of data-driven, machine-learned models can be seen as a process that discloses the relationships between their input and output. These relationships consist and can be represented as a set of inference rules. However, the models usually do not explicit these rules to their end-users who, subsequently, perceive them as black-boxes and might not trust their predictions. Therefore, scholars have proposed several methods for extracting rules from data-driven machine-learned models to explain their logic. However, limited work exists on the evaluation and comparison of these methods. This study proposes a novel comparative approach to evaluate and compare the rulesets produced by five model-agnostic, post-hoc rule extractors by employing eight quantitative metrics. Eventually, the Friedman test was employed to check whether a method consistently performed better than the others, in terms of the selected metrics, and could be considered superior. Findings demonstrate that these metrics do not provide sufficient evidence to identify superior methods over the others. However, when used together, these metrics form a tool, applicable to every rule-extraction method and machine-learned models, that is, suitable to highlight the strengths and weaknesses of the rule-extractors in various applications in an objective and straightforward manner, without any human interventions. Thus, they are capable of successfully modelling distinctively aspects of explainability, providing to researchers and practitioners vital insights on what a model has learned during its training process and how it makes its predictions

TİROİD VE KRONİK BÖBREK HASTALIĞI VERİLERİNİN SINIFLANDIRILMASINDA GENETİK ALGORİTMALAR VE PCA İLE HİBRİT ÖZELLİK SEÇİMİ

TİROİD VE KRONİK BÖBREK HASTALIĞI VERİLERİNİN SINIFLANDIRILMASINDA GENETİK ALGORİTMALAR VE PCA İLE HİBRİT ÖZELLİK SEÇİMİÖzetBu çalışmada tiroid ve kronik böbrek hastalığının teşhisinde k-nearest neighbors sınıflandırıcının performansını arttırmak amacıyla genetik algoritmalar ve temel bileşenler analizi (PCA) hibrit şekilde kullanılmış ve yeni bir özellik seçimi yöntemi önerilmiştir. Hibrit özellik seçimi yönteminde elde edilen uygulama sonuçları, veri setlerinin özellik seçimi uygulanmamış başlangıç performansıyla karşılaştırılmıştır. Sonuç olarak önerilen hibrit metotla birlikte sınıflandırma başarısı tiroid veri seti için %93.44’ten %95.89’a, böbrek veri seti için %93.75’ten %98.25’e çıkarılmıştır. Sonuçların tutarlı olması için her iki veri setine 10-kat çapraz doğrulama yapılmıştır.Anahtar Kelimeler: Genetik Algoritmalar, PCA, Özellik Seçimi, K-nearest neighborsHYBRID FEATURE SELECTION USING GENETIC ALGORITHMS AND PCA IN CLASSIFICATION OF THYROID AND CHRONIC KIDNEY DISEASE DATAAbstractIn this study, genetic algorithms and principal component analysis (PCA) were used in a hybrid way to increase the performance of the k-nearest neighbors classifier in the diagnosis of thyroid and chronic kidney disease, and a new feature selection method was proposed. The application results obtained in the hybrid feature selection method were compared with the initial performance of the data sets before the feature selection was applied. As a result, with the proposed hybrid method, the classification success was increased from 93.44% to 95.89% for the thyroid data set and from 93.75%to 98.25%for the kidney data set. A 10-fold cross validation was applied to both data sets to ensure consistent results.Keywords: Genetic Algorithms, PCA, Feature Selection, K-nearest neighbor

Computer-Aided Diagnosis of Thyroid Nodule: A Review

ABSTRAC

The Right Direction Needed to Develop White-Box Deep Learning in Radiology, Pathology, and Ophthalmology: A Short Review

The popularity of deep learning (DL) in the machine learning community has been dramatically increasing since 2012. The theoretical foundations of DL are well-rooted in the classical neural network (NN). Rule extraction is not a new concept, but was originally devised for a shallow NN. For about the past 30 years, extensive efforts have been made by many researchers to resolve the “black box” problem of trained shallow NNs using rule extraction technology. A rule extraction technology that is well-balanced between accuracy and interpretability has recently been proposed for shallow NNs as a promising means to address this black box problem. Recently, we have been confronting a “new black box” problem caused by highly complex deep NNs (DNNs) generated by DL. In this paper, we first review four rule extraction approaches to resolve the black box problem of DNNs trained by DL in computer vision. Next, we discuss the fundamental limitations and criticisms of current DL approaches in radiology, pathology, and ophthalmology from the black box point of view. We also review the conversion methods from DNNs to decision trees and point out their limitations. Furthermore, we describe a transparent approach for resolving the black box problem of DNNs trained by a deep belief network. Finally, we provide a brief description to realize the transparency of DNNs generated by a convolutional NN and discuss a practical way to realize the transparency of DL in radiology, pathology, and ophthalmology

Protein Fold Recognition Using Adaboost Learning Strategy

Protein structure prediction is one of the most important and difficult problems in computational molecular biology. Unlike sequence-only comparison, protein fold recognition based on machine learning algorithms attempts to detect similarities between protein structures which might not be accompanied with any significant sequence similarity. It takes advantage of the information from structural and physic properties beyond sequence information. In this thesis, we present a novel classifier on protein fold recognition, using AdaBoost algorithm that hybrids to k Nearest Neighbor classifier. The experiment framework consists of two tasks: (i) carry out cross validation within the training dataset, and (ii) test on unseen validation dataset, in which 90% of the proteins have less than 25% sequence identity in training samples. Our result yields 64.7% successful rate in classifying independent validation dataset into 27 types of protein folds. Our experiments on the task of protein folding recognition prove the merit of this approach, as it shows that AdaBoost strategy coupling with weak learning classifiers lead to improved and robust performance of 64.7% accuracy versus 61.2% accuracy in published literatures using identical sample sets, feature representation, and class labels