High Dimensional Classification with combined Adaptive Sparse PLS and Logistic Regression

Motivation: The high dimensionality of genomic data calls for the development of specific classification methodologies, especially to prevent over-optimistic predictions. This challenge can be tackled by compression and variable selection, which combined constitute a powerful framework for classification, as well as data visualization and interpretation. However, current proposed combinations lead to instable and non convergent methods due to inappropriate computational frameworks. We hereby propose a stable and convergent approach for classification in high dimensional based on sparse Partial Least Squares (sparse PLS). Results: We start by proposing a new solution for the sparse PLS problem that is based on proximal operators for the case of univariate responses. Then we develop an adaptive version of the sparse PLS for classification, which combines iterative optimization of logistic regression and sparse PLS to ensure convergence and stability. Our results are confirmed on synthetic and experimental data. In particular we show how crucial convergence and stability can be when cross-validation is involved for calibration purposes. Using gene expression data we explore the prediction of breast cancer relapse. We also propose a multicategorial version of our method on the prediction of cell-types based on single-cell expression data. Availability: Our approach is implemented in the plsgenomics R-package.Comment: 9 pages, 3 figures, 4 tables + Supplementary Materials 8 pages, 3 figures, 10 table

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis

This paper proposes an adaptive evolutionary radial basis function (RBF) network algorithm to evolve accuracy and connections (centers and weights) of RBF networks simultaneously. The problem of hybrid learning of RBF network is discussed with the multi-objective optimization methods to improve classification accuracy for medical disease diagnosis. In this paper, we introduce a time variant multi-objective particle swarm optimization (TVMOPSO) of radial basis function (RBF) network for diagnosing the medical diseases. This study applied RBF network training to determine whether RBF networks can be developed using TVMOPSO, and the performance is validated based on accuracy and complexity. Our approach is tested on three standard data sets from UCI machine learning repository. The results show that our approach is a viable alternative and provides an effective means to solve multi-objective RBF network for medical disease diagnosis. It is better than RBF network based on MOPSO and NSGA-II, and also competitive with other methods in the literature

Network Lasso: Clustering and Optimization in Large Graphs

Convex optimization is an essential tool for modern data analysis, as it provides a framework to formulate and solve many problems in machine learning and data mining. However, general convex optimization solvers do not scale well, and scalable solvers are often specialized to only work on a narrow class of problems. Therefore, there is a need for simple, scalable algorithms that can solve many common optimization problems. In this paper, we introduce the \emph{network lasso}, a generalization of the group lasso to a network setting that allows for simultaneous clustering and optimization on graphs. We develop an algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in a distributed and scalable manner, which allows for guaranteed global convergence even on large graphs. We also examine a non-convex extension of this approach. We then demonstrate that many types of problems can be expressed in our framework. We focus on three in particular - binary classification, predicting housing prices, and event detection in time series data - comparing the network lasso to baseline approaches and showing that it is both a fast and accurate method of solving large optimization problems

Automated Optimization Deep Learning Model for Assessment and Guidance System Through Natural Language Processing with Reduction of Anxiety Among Students

The Assisted Assessment and Guidance System serves as a valuable tool in supporting individuals' learning, growth, and development. The Assisted Assessment and Guidance System with Natural Language Processing (NLP) is an innovative software application designed to provide personalized and intelligent support for assessment and guidance processes in various domains. NLP techniques are employed to analyze and understand human language, allowing the system to extract valuable insights from text-based data and provide tailored feedback and guidance. This paper proposed an Integrated Optimization Directional Clustering Classification (IODCc) for assessment of the foreign language anxiety. Additionally, the paper introduces an Integrated Optimization Directional Clustering Classification (IODCc) approach for assessing foreign language anxiety. This approach incorporates two optimization models, namely Black Widow Optimization (BWO) and Seahorse Optimization (SHO). BWO and SHO are metaheuristic optimization algorithms that simulate the behaviors of black widow spiders and seahorses, respectively, to improve the accuracy of the assessment process. The integration of these optimization models within the IODCc approach aims to enhance the accuracy and effectiveness of the foreign language anxiety assessment. Simulation analysis is performed for the data collected from the 1000 foreign language students. The experimental analysis expressed that the proposed IODCc model achieves an accuracy of 99% for the classification. The findings suggested that through pre-training of languages, the anxiety of the students will be reduced