THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

The organization of behavior: A neuropsychological theory

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data-mining and machine-learning problems. The objectives of feature selection include building simpler and more comprehensible models, improving data-mining performance, and preparing clean, understandable data. The recent proliferation of big data has presented some substantial challenges and opportunities to feature selection. In this survey, we provide a comprehensive and structured overview of recent advances in feature selection research. Motivated by current challenges and opportunities in the era of big data, we revisit feature selection research from a data perspective and review representative feature selection algorithms for conventional data, structured data, heterogeneous data and streaming data. Methodologically, to emphasize the differences and similarities of most existing feature selection algorithms for conventional data, we categorize them into four main groups: similarity-based, information-theoretical-based, sparse-learning-based, and statistical-based methods. To facilitate and promote the research in this community, we also present an open source feature selection repository that consists of most of the popular feature selection algorithms (http://featureselection.asu.edu/). Also, we use it as an example to show how to evaluate feature selection algorithms. At the end of the survey, we present a discussion about some open problems and challenges that require more attention in future research.

https://dl.acm.org/doi/pdf/10.1145/3136625

Feature Selection: A Data Perspective

Feature selection in machine learning: A new perspective

Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up to hundreds of thousands of features with relatively small sample size. Because learning algorithms usually do not work well with this kind of data, a challenge to reduce the data dimensionality arises. A huge number of gene selection are applied to select a subset of relevant features for model construction and to seek for better cancer classification performance. This paper presents the basic taxonomy of feature selection, and also reviews the state-of-the-art gene selection methods by grouping the literatures into three categories: supervised, unsupervised, and semi-supervised. The comparison of experimental results on top 5 representative gene expression datasets indicates that the classification accuracy of unsupervised and semi-supervised feature selection is competitive with supervised feature selection.

Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection

Segmentation of the breast region and pectoral muscle are fundamental subsequent steps in the process of Computer-Aided Diagnosis (CAD) systems. Segmenting the breast region and pectoral muscle are considered a difficult task, particularly in mammogram images because of artefacts, homogeneity among the region of the breast and pectoral muscle, and low contrast along the region of breast boundary, the similarity between the texture of the Region of Interest (ROI), and the unwanted region and irregular ROI. This study aims to propose an improved threshold-based and trainable segmentation model to derive ROI. A hybrid segmentation approach for the boundary of the breast region and pectoral muscle in mammogram images was established based on thresholding and Machine Learning (ML) techniques. For breast boundary estimation, the region of the breast was highlighted by eliminating bands of the wavelet transform. The initial breast boundary was determined through a new thresholding technique. Morphological operations and masking were employed to correct the overestimated boundary by deleting small objects. In the medical imaging field, significant progress to develop effective and accurate ML methods for the segmentation process. In the literature, the imperative role of ML methods in enabling effective and more accurate segmentation method has been highlighted. In this study, an ML technique was built based on the Histogram of Oriented Gradient (HOG) feature with neural network classifiers to determine the region of pectoral muscle and ROI. The proposed segmentation approach was tested by utilizing 322, 200, 100 mammogram images from mammographic image analysis society (mini-MIAS), INbreast, Breast Cancer Digital Repository (BCDR) databases, respectively. The experimental results were compared with manual segmentation based on different texture features. Moreover, evaluation and comparison for the boundary of the breast region and pectoral muscle segmentation have been done separately. The experimental results showed that the boundary of the breast region and the pectoral muscle segmentation approach obtained an accuracy of 98.13% and 98.41% (mini-MIAS), 100%, and 98.01% (INbreast), and 99.8% and 99.5% (BCDR), respectively. On average, the proposed study achieved 99.31% accuracy for the boundary of breast region segmentation and 98.64% accuracy for pectoral muscle segmentation. The overall ROI performance of the proposed method showed improving accuracy after improving the threshold technique for background segmentation and building an ML technique for pectoral muscle segmentation. More so, this article also included the ground-truth as an evaluation of comprehensive similarity. In the clinic, this analysis may be provided as a valuable support for breast cancer identification.

/pdf/improved-threshold-based-and-trainable-fully-automated-5bdy76weij.pdf

Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images

https://www.techscience.com/cmc/v66n3/41066/pdf

Multi-Level Fusion in Ultrasound for Cancer Detection based on Uniform LBP Features

Algoritma rambatan balik telah digunakan secara meluas dalam menyelesaikan pelbagai masalah dengan menggunakan konsep perceptron multi aras. Namun begitu terdapat banyak isu utama pada algoritma ini seperti kadar penumpuan yang lambat dan kekerapan terperangkap di dalam minimum setempat. Bagi mengatasi masalah ini, Algoritma Genetik (AG) digunakan untuk menentukan nilai yang optimal bagi mendapatkan parameter yang sesuai seperti kadar pembelajaran dan kadar momentum serta pengoptimuman pemberat. Walaupun AG berjaya meningkatkan keupayaan pembelajaran bagi Rangkaian Neural (RN) menggunakan rambatan balik, masih terdapat beberapa masalah lain seperti latihan untuk mengeluarkan output mengambil masa yang lama dan penggunaan fungsi yang rumit seperti perhitungan pilihan, silangan dan mutasi. Kajian ini mengemukakan teknik pengoptimuman yang terkini, iaitu pengoptimuman partikel secara berkumpulan yang dicerap di dalam proses pembelajaran RN, bagi meningkatkan masa penumpuan dan ketepatan pengelasan. Dua uji kaji telah dilaksanakan, iaitu RN ke hadapan menggunakan pengoptimuman partikel secara berkumpulan dan RN rambatan balik menggunakan AG. Hasil kajian mendapati bahawa RN ke hadapan dengan pengoptimuman partikel secara berkumpulan memberikan keputusan yang lebih baik dari aspek masa penumpuan dan ketepatan pengelasan, berbanding dengan RN rambatan balik menggunakan AG.
 Kata kunci: Partikel berkumpulan; rangkaian neural; algoritma genetik; rambatan balik; kepintaran berkumpulan
				
				Backpropagation (BP) algorithm is widely used to solve many real world problems by using the concept of Multilayer Perceptron (MLP). However, major disadvantages of BP are its convergence rate is relatively slow and always being trapped at the local minima. To overcome this problem, Genetic Algorithm (GA) has been used to determine optimal value for BP parameters such as learning and momentum rate and, also for weight optimization. Although GA has successfully improved Backpropagation Neural Network (BPNN) learning, there are still some issues such as longer training time to produce the output and usage of complex functions in selection, crossover and mutation calculation. In this study, Particle Swarm Optimization (PSO) algorithm has been chosen and applied in feedforward neural network to enhance the learning process in terms of convergence rate and classification accuracy. Two experiments have been conducted; Particle Swarm Optimization Feedforward Neural Network (PSONN) and Genetic Algorithm Backpropagation Neural Network (GANN). The results show that PSONN give promising results in terms of convergence rate and classification accuracy compared to GANN.
				
				Key words: Particle swarm; neural network; genetic algorithm; backpropagation; swarm intelligence

Particle Swarm Optimization For Neural Network Learning Enhancement

The paper deals with feature (variable) and model parameter optimisation utilising a proposed dynamic quantum–inspired particle swarm optimisation method In this method the features of the model are represented probabilistically as a quantum bit vector and the model parameter values – as real numbers The principle of quantum superposition is used to accelerate the search for an optimal set of features, that combined through co-evolution with a set of optimised parameter values, will result in an optimal model The paper applies the method to the problem of feature and parameter optimisation of evolving spiking neural network models A swarm of particles is used to find the classification model with the best accuracy for a given classification task The method is illustrated on a bench mark classification problem The proposed method results in the design of faster and more accurate classification models than the ones optimised with the use of standard evolutionary optimisation algorithms

Haza Nuzly Abdull Hamed

Papers

Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection

Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images

Multi-Level Fusion in Ultrasound for Cancer Detection based on Uniform LBP Features

Particle Swarm Optimization For Neural Network Learning Enhancement

Quantum-inspired Particle Swarm Optimisation for Integrated Feature and Parameter Optimisation of Evolving Spiking Neural Networks