Query: survay paper on compariosn algorithm on machine learning

Candidate papers:

abstract: Machine learning models of materials1 5 accelerate discovery compared to ab initio methods: deep learning models now reproduce density functional theory (DFT) calculated results at one hundred thousandths of the cost of DFT6. To provide guidance in experimental materials synthesis, these need to be coupled with an accurate yet effective search algorithm and training data consistent with experimental observations. Here we report an evolutionary algorithm powered search which uses machine learned surrogate models trained on highthroughput hybrid functional DFT data benchmarked against experimental bandgaps: Deep Adaptive Regressive Weighted Intelligent Network (DARWIN) The strategy enables efficient search over the materials space of ~108 ternaries and 1011 quaternaries7 for candidates with target properties. It provides interpretable design rules, such as our finding that the difference in the electronegativity between the halide and B site cation being a strong predictor of ternary structural stability. As an example, when we seek UV emission, DARWIN predicts K2CuX3 (X Cl, Br) as a promising materials family, based on its electronegativity difference. We synthesized and found these materials to be stable, direct bandgap UV emitters. The approach also allows knowledge distillation for use by humans. Introduction Inverse materials design, i.e. prediction of a structure and composition with targeted properties, is used to accelerate materials discovery for light emission, sensing, lasing, energy harvesting and energy storage. Recently, deep learning models have advanced in predicting the materials properties of molecular and inorganic crystal structures8 10. However, even with a deep learning acceleration of 105, such brute force exploration of the compositional and structural space remains infeasible: there are ~108 inorganic ternary, ~1011 quaternary compounds, and even more variations for alloyed and multinary compositions. An efficient and interpretable search algorithm is required to complement accurate property evaluation. This combinatorial space may have multiple local optima, hindering the efficiency of gradient based methods. Searching within a lower dimensional latent space has been suggested previously11,12, yet the continuous nature of the latent space distinct from the discrete nature of chemical elements leads to multiple predictions based on artificial elements, which when discretized to true elements do not present the optimal solutions due to the sampling methods involved in prediction. Evolutionary algorithms (EA) are a gradient free bias free approach that allow for the rapid exploration of a space13,14. They are particularly well suited to search when the evaluation cost per instance is small compared to the size of the space15,16. The evolutionary algorithm approach assesses an entire population at each specific time step, giving rise to multiple local solutions while iterating towards the global minima17. Furthermore, the approach is highly flexible, allowing for the multi objective optimization of mixed variables (discrete and continuous) thereby enabling efficient space exploration in which typical gradient based approaches suffer from18,19. Discussion Several studies have discussed machine learning (ML) algorithms for accelerated discovery of novel materials, but a major hurdle in actually discovering a new material is the availability of high fidelity training data. All the open source material databases20 22 report Perdew Burke Ernzerhof (PBE)23 exchange correlation based calculated Density Functional Theory (DFT) bandgaps which are known to severely and inconsistently underestimate24 the bandgaps of the materials. ML models trained on such data will yield error prone and unreliable predictions unable to guide the experiments. This has made most studies6,25 on accelerated materials discovery focus only on the stability of the materials, ignoring the optical properties (bandgap and nature of bandgaps) Using a generated high throughput hybrid exchange correlation based bandgaps database (Supplementary Figures 1 3) we bridge this gap between theory and experiments (Figure 1 where we compare ML predictions with experimental bandgap values) We design a machine learned framework DARWIN: Deep Adaptive Regressive Weighted Intelligent Network. It enables efficient search over a large chemical space using our ML based models and proposes a diverse set of candidates with specific target properties. We develop a graph convolutional neural network (GCNN) architecture6,10 that incorporates a unique edge attribute, which enables efficient learning on a smaller dataset. This specific reciprocal distance edge attribute helps capture the strength of the relative atomic properties for the varied elements in the crystal system. GCNN generates a global representation of the crystal structure from the chemical features representative of each element at a given node and edge feature that scales as the reciprocal distance between the atoms to reproduce the predictions of accurate ab initio calculations (Figure 1a, see Methods for details) Using data from the Materials Project database14,26, we trained a total energy regressor and a classifier for predicting the direct/indirect nature of bandgaps (Supplementary Figures 4 8 for loss and classification curves) We train our bandgap regressor (Supplementary Figure 9, 10) on a newly generated dataset of 1800 samples (which we release as part of this paper) calculated using HSE functional, known to correlate well with experimental values. We report an original performance comparison between ML predictions and experimental values of the bandgaps (Figure 1b) Despite training on DFT values, ML predicted bandgaps show good agreement with measured values on a test set of experimentally confirmed compounds. The trained and optimized GCNN models predict HSE06 bandgaps with mean absolute errors (MAE) of 0.53 eV on test data, 0.45 eV on validation data, and energies above hull with MAE of 0.06 eV/atom (Figure 1b, c) Furthermore, we build an accurate classifier for predicting the directindirect nature of the materials and obtain an F1 score of 0.8 (Figure 1d) This value is close to previously reported (0.89) study on the direct indirect classification that was limited only to the Kesterite family of compounds27. Using GCNNs allowed going beyond a single crystal family and developing a dependable and accurate classifier for all 7 crystal systems. (Figure 1e for a comparison between ML predictions and DFT)
author_names: Hitarth Choubisa

Petar Todorovi'c

Joao M Pina

Darshan H Parmar

Ziliang Li

Oleksandr Voznyy

Isaac Tamblyn

Edward Sargent Department of Electrical

Computer Engineering

University of Toronto

National Research Council Canada

Vector Institute for Artificial Intelligence

Department of Physical

Environmental Sciences
title: Interpretable discovery of new semiconductors with machine learning
venue: ArXiv
year: 2021

abstract: MicroRNAs are small non coding RNA found in many different species. They play crucial roles in cancer such as biological processes of apoptosis and proliferation. The identification of microRNA target genes can be an essential first step towards to reveal the role of microRNA in various cancer types. In this paper, we predict miRNA target genes for lung cancer by integrating prediction scores from miRanda and PITA algorithms used as a feature vector of miRNA target interaction. Then, machine learning algorithms were implemented for making a final prediction. The approach developed in this study should be of value for future studies into understanding the role of miRNAs in molecular mechanisms enabling lung cancer formation. Keywords MicroRNA, miRNAs, lung cancer, machine learning, Naive Bayes, SVM.
author_names: Nilubon Kurubanjerdjit

Nattakarn Iam-On

Ka-Lok Ng
title: Prediction of MicroRNA Target Gene by Machine Learning Algorithms in Lung Cancer Study
year: 2016

abstract: Massive Multiple Input Multiple Output (MIMO) is widely considered a pivotal communication technology for future generations of wireless networks. Massive MIMO uses a large number of antennas at the base station, which offers better effectiveness in spectral and energy use. However, a Frequency Division Duplex (FDD) system is challenging in reciprocity since it is difficult to estimate channels and requires feeding back channel state information. Joint Spatial Division and Multiplexing (JSDM) is a simplified FDD technique to provide massive MIMO gains. The main idea of JSDM is related to grouping users with approximately similar channel covariance. Many machine learning algorithms have been applied to conduct user grouping. In this paper, to improve the user grouping, we employ Reinforcement Guided Competitive Learning (RGCL) to the user grouping and then compare it with clustering techniques, including K means, and sequential K means to achieve the appropriate user grouping. The experimental results show that the RGCL technique represents better performance in computational time and system throughput than the other two above mentioned techniques, since RGCL can avoid being trapping in local minima.
author_names: Tanyaluk Deeka

Boriboon Deeka

Surajate On-rit
title: A Study of a Competitive Reinforcement Learning Approach for Joint Spatial Division and Multiplexing in Massive MIMO
year: 2021

abstract: The performance of different machine learning algorithms for detecting the nature of change was compared. To choose the best algorithms for a specific task, users are advised to consider not only classification accuracy, but also comprehensibility, compactness, and robustness in training and classification. The purpose of this paper is to provide information to users so that they can choose the best algorithms for specific tasks. To alleviate the problem of obtaining enough training data, simulated training data were generated from single date images. A one pass classification with four machine learning algorithms, namely, Multi Layer Perceptrons (MLP) Learning Vector Quantization (LVQ) Decision Tree Classifiers (DTC) and the Maximum Likelihood Classifier (MLC) were tested. Recognition rates, ease of use, and degree of automation of the four algorithms were assessed. The results showed that the incorporation of cross combined simulated training data enhanced the detection of nature of change. Compared to conventional post classification comparison methods, LVQ and DTC did better in terms of overall accuracy. In terms of average accuracy of the change classes, LVQ was the best performer. DTC was the easiest to use and the most robust in training. MLP procedures were the most difficult to replicate.
author_names: Jonathan Cheung-Wai Chan

Kwok-Ping Chan

Anthony Gar-on Yeh
title: Detecting the nature of change in an urban environment A comparison of machine learning algorithms
year: 2001

abstract: ABSTRACT Urban villages (UVs) are commonly found in many Asian cities. These villages contain many closely packed buildings constructed decades ago without proper urban planning. There is a need for those buildings to be identified and put into statistics. In this paper, we present a segmentation framework that invokes multiple machine learning techniques and point cloud/image processing algorithms to segment individual closely packed buildings from large urban scenes. The presented framework consists of two major segmentation processes. The framework first filters out the non ground objects from the point cloud, then it classified them by using the Random Forest classifier to isolate buildings from the entire scene. After that, the building point clouds will be segmented based on several building attribute analysis methods. This is followed by using the Random Sample Consensus (RANSAC) plane filtering method to expand the space between two closely packed buildings, so that the Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering technique can be used to more accurately segment each individual building from the closely packed building areas. Two airborne Light Detection and Ranging (LiDAR) datasets collected in two different cities with some typical closely packed buildings were used to verify the proposed framework. The results show that the framework can effectively identify the closely packed buildings with unified structures from large airborne LiDAR datasets. The overall segmentation accuracy reaches 84% for the two datasets. The proposed framework can serve as a basis for analysis and segmentation of closely packed buildings with a more complicated structure.
author_names: Xinsheng Wang

Ting On Chan

Kai Liu

Jun Pan

Ming Luo

Wenkai Li

Chunzhu Wei
title: A robust segmentation framework for closely packed buildings from airborne LiDAR point clouds
year: 2020

abstract: Pedagogy with learning analytics is shown to facilitate the teaching learning process through analyzing student's behaviours. In this paper, we explored the possibility of using learning analytics tools Coh Metrix and Lightside for analyzing and improving writing skills of students in a technological common core curriculum course. In this study, we i) investigated linguistic characteristics of student's essays, and ii) applied a machine learning algorithm for giving instant sketch feedback to students. Results illustrated the necessity of improving student's writing skills in their university learning through elearning technologies, so that students can effectively circulate their ideas to the public in the future.
author_names: Chi-Un Lei

Ka Lok Man

Tiew On Ting
title: Using Learning Analytics to Analyze Writing Skills of Students A Case Study in a Technological Common Core Curriculum Course
year: 2014

abstract: Feature Ordering is a unique preprocessing step in Incremental Attribute Learning (IAL) where features are gradually trained one after another. In previous studies, feature ordering derived based upon each individual feature's contribution is time consuming. This study attempts to develop an efficient feature ordering algorithm by some evolutionary approaches. The feature ordering algorithm presented in this paper is based on a criterion of maximum mean of feature discriminability. Experimental results derived by ITID, a neural IAL algorithm, show that such a feature ordering algorithm has a higher probability to obtain the lowest classification error rate with datasets from UCI Machine Learning Repository.
author_names: Ting Wang

Steven Guan

Tiew On Ting

Ka Lok Man

Fei Liu
title: Evolving Linear Discriminant in a Continuously Growing Dimensional Space for Incremental Attribute Learning
venue: NPC
year: 2012

abstract: Discretization plays a major role as a data preprocessing technique used in machine learning and data mining. Recent studies have focused on multivariate discretization that considers relations among attributes. The general goal of this method is to obtain the discrete data, which preserves most of the semantics exhibited by original continuous data. However, many techniques generate the final discrete data that may be less useful with natural groups of data not being maintained. This paper presents a novel graph clustering based discretization algorithm that encodes different similarity measures into a graph representation of the examined data. The intuition allows more refined data wise relations to be obtained and used with the effective graph clustering technique based on normalized association to discover nature graphs accurately. The goodness of this approach is empirically demonstrated over 30 standard datasets and 20 imbalanced datasets, compared with 11 well known discretization algorithms using 4 classifiers. The results suggest the new approach is able to preserve the natural groups and usually achieve the efficiency in terms of classifier performance, and the desired number of intervals than the comparative methods.
author_names: Kittakorn Sriwanna

Tossapon Boongoen

Natthakan Iam-on
title: Graph clustering based discretization of splitting and merging methods (GraphS and GraphM)
venue: Human centric Computing and Information Sciences
year: 2017

abstract: Feature selection (FS) plays a crucial role in machine learning to build a robust model for either learning or classification from a large amount of data. Among feature selection techniques, the Relief algorithm is one of the most common due to its simplicity and effectiveness. The performance of the Relief algorithm, however, could be dramatically affected by the consistency of the data patterns. For instance, Relief F could become less accurate in the presence of noise. The accuracy would decrease further if an outlier sample was included in the dataset. Therefore, it is very important to select the samples to be included in the dataset carefully. This paper presents an effort to improve the effectiveness of Relief algorithm by filtering samples before selecting features. This method is termed Sample Filtering Relief Algorithm (SFRA) The main idea of this method is to discriminate outlier samples out of the main pattern using self organizing map (SOM) and then proceed with feature selection using the Relief algorithm. We have tested SFRA with a gene expression dataset of interferon (IFN response of Hepatitis B patients that contains outlier data. SFRA could successfully remove outlier samples that have been verified by visual inspection by experts. Also, it has better accuracy in separating the relevant and irrelevant features than other feature selection methods considered.
author_names: Thammakorn Saethang

Santitham Prom-on

Asawin Meechai

Jonathan H Chan
title: Sample Filtering Relief Algorithm: Robust Algorithm for Feature Selection
venue: ICONIP
year: 2008

abstract: The paper presents a novel search algorithm named adaptive tabu search (ATS) The algorithm has been applied to enhance the learning process of neural networks. The paper presents a new neuro tabu fuzzy (NTF) control structure to demonstrate the capability of the ATS algorithm. The algorithm is general and can be applied to various problems including machine learning, optimization, etc. Our proposed algorithm and controller nicely stabilize the single and double inverted pendulum systems.
author_names: Sarawut Sujitjorn

Sudarat Khwan-on
title: Learning Control Via Neuro Tabu Fuzzy Controller
venue: KES
year: 2006