Multi-objective Selection Model Research Articles

Multi-objective model selection algorithm for online sequential ultimate learning machine

In order to improve the channel equalization, improve the control quality, and reduce the error of the target output of the online sequential learning machine, a multi-objective model selection algorithm is proposed based on feedback compensation and adaptive equalization control. The channel equalization model of online sequential ultimate learning machine is constructed. The sensor fusion information of online sequential limit learning machine is selected adaptively by multi-objective combined control, and the multi-objective combined control is carried out by using matched filtering method. Combined with feedback compensation and adaptive equalization control method, the classification selection and equalization of network multi-objective models are realized. The simulation results show that the algorithm has good accuracy in classifying and selecting multi-objective models of online sequential LLM, the adaptive equalization performance of the channel is better, and the error of LLM control is low.

Multi-Objective Model Selection for Unmanned Aerial Vehicles Automatic Target Recognition Systems

Within unmanned aerial vehicles on-going research topics, automatic target recognition is acquiring relevance. This is due to the easiness with which it is possible to acquire such devices. In order to do this, signal processing and classification techniques could be adopted. Also, in order to improve the classification ratio, optimization techniques could be used. This subject is object of study for many researchers, but the authors worry about the lack of information while using single-objective optimization techniques. The proposed work comprises the application of multi-objective optimization design in order to create an automatic target recognition system to discriminate different types of unmanned aerial vehicles. In order to accomplish this, a K-band radar system is used to send and receive electromagnetic signals, which are then processed with feature extraction techniques, and finally, applied on an artificial neural network system. In order to improve the system’s classification ratio, the classifier is defined as a multi-objective problem, and evolutionary multi-objective optimization techniques are applied. Finally, in order to select the best possible trade-off, the level diagrams multi-criteria decision making methodology is used to compare different solutions.

Pareto-Optimal Model Selection via SPRINT-Race.

In machine learning, the notion of multi-objective model selection (MOMS) refers to the problem of identifying the set of Pareto-optimal models that optimize by compromising more than one predefined objectives simultaneously. This paper introduces SPRINT-Race, the first multi-objective racing algorithm in a fixed-confidence setting, which is based on the sequential probability ratio with indifference zone test. SPRINT-Race addresses the problem of MOMS with multiple stochastic optimization objectives in the proper Pareto-optimality sense. In SPRINT-Race, a pairwise dominance or non-dominance relationship is statistically inferred via a non-parametric, ternary-decision, dual-sequential probability ratio test. The overall probability of falsely eliminating any Pareto-optimal models or mistakenly returning any clearly dominated models is strictly controlled by a sequential Holm's step-down family-wise error rate control method. As a fixed-confidence model selection algorithm, the objective of SPRINT-Race is to minimize the computational effort required to achieve a prescribed confidence level about the quality of the returned models. The performance of SPRINT-Race is first examined via an artificially constructed MOMS problem with known ground truth. Subsequently, SPRINT-Race is applied on two real-world applications: 1) hybrid recommender system design and 2) multi-criteria stock selection. The experimental results verify that SPRINT-Race is an effective and efficient tool for such MOMS problems. code of SPRINT-Race is available at https://github.com/watera427/SPRINT-Race.

Multi-Objective Model Selection (MOMS)-based Semi-Supervised Framework for Sentiment Analysis

Sentiment analysis has emerged as an active research field due to the rapid growth of user-generated content on the Internet. This research area analyzes the opinions and attitudes of masses toward products, movies, topics, individuals, and services. Various machine learning and text mining algorithms have been used for sentiment analysis and classification. The recent research concludes that domain-specific lexicons perform significantly better as compared to domain-independent lexicons. The proposed research aims at improving the performance of general-purpose lexicons utilizing machine learning algorithms. A semi-supervised framework based on “MOMS” is introduced in order to determine the feature weight by incorporating SentiWordNet, a well-known general-purpose sentiment lexicon. The feature weights are learned by support vector machine, and the classification performance is enhanced by using Multi-Objective Model Selection procedure. Subjectivity criterion is used to select the desired features, and the effects of feature selection with respect to their part-of-speech information are studied comprehensively. Experimental evaluation is performed on seven different benchmark datasets which includes Large movie review dataset, Multi-domain sentiment dataset, and Cornell movie review dataset. The comparison of the proposed approach is performed with state-of-the-art techniques, lexicon-based approaches, and other methods for sentiment analysis. The proposed framework results in high performance when compared to other research in this field.

Multi-Objective Model Selection via Racing.

Model selection is a core aspect in machine learning and is, occasionally, multi-objective in nature. For instance, hyper-parameter selection in a multi-task learning context is of multi-objective nature, since all the tasks' objectives must be optimized simultaneously. In this paper, a novel multi-objective racing algorithm (RA), namely S-Race, is put forward. Given an ensemble of models, our task is to reliably identify Pareto optimal models evaluated against multiple objectives, while minimizing the total computational cost. As a RA, S-Race attempts to eliminate non-promising models with confidence as early as possible, so as to concentrate computational resources on promising ones. Simultaneously, it addresses the problem of multi-objective model selection (MOMS) in the sense of Pareto optimality. In S-Race, the nonparametric sign test is utilized for pair-wise dominance relationship identification. Moreover, a discrete Holm's step-down procedure is adopted to control the family-wise error rate of the set of hypotheses made simultaneously. The significance level assigned to each family is adjusted adaptively during the race. In order to illustrate its merits, S-Race is applied on three MOMS problems: 1) selecting support vector machines for classification; 2) tuning the parameters of artificial bee colony algorithms for numerical optimization; and 3) constructing optimal hybrid recommendation systems for movie recommendation. The experimental results confirm that S-Race is an efficient and effective MOMS algorithm compared to a brute-force approach.

Surrogate-assisted multi-objective model selection for support vector machines

Classification is one of the most well-known tasks in supervised learning. A vast number of algorithms for pattern classification have been proposed so far. Among these, support vector machines (SVMs) are one of the most popular approaches, due to the high performance reached by these methods in a wide number of pattern recognition applications. Nevertheless, the effectiveness of SVMs highly depends on their hyper-parameters. Besides the fine-tuning of their hyper-parameters, the way in which the features are scaled as well as the presence of non-relevant features could affect their generalization performance. This paper introduces an approach for addressing model selection for support vector machines used in classification tasks. In our formulation, a model can be composed of feature selection and pre-processing methods besides the SVM classifier. We formulate the model selection problem as a multi-objective one, aiming to minimize simultaneously two components that are closely related to the error of a model: bias and variance components, which are estimated in an experimental fashion. A surrogate-assisted evolutionary multi-objective optimization approach is adopted to explore the hyper-parameters space. We adopted this approach due to the fact that estimating the bias and variance could be computationally expensive. Therefore, by using surrogate-assisted optimization, we expect to reduce the number of solutions evaluated by the fitness functions so that the computational cost would also be reduced. Experimental results conducted on benchmark datasets widely used in the literature, indicate that highly competitive models with a fewer number of fitness function evaluations are obtained by our proposal when it is compared to state of the art model selection methods.

A Theme-Based and Network Coupled Framework for Quantatitive Indicators Selection in Ecological Construction: A Case Study in Fujian, China

It is important to build a scientific and effective indicator system for the assessment and management of regional ecological construction. A theme-based and network coupled framework provides a useful approach to solve some problems in establishing indicator systems, such as lack of systematic study on indicators connotation and non-transparent processes of indicators selection. Based on the ecological construction practice in Fujian province, China, this paper (1) analyzed the main challenges of the ecological construction,implementation and development processes, summarized the currently main themes for ecological construction, and built a theme-based indicators framework; (2) established indicators selection criteria matrix and alternative indicators matrix by using network analysis method; (3) built a multi-objective selection model considering the economic cost, social effect and ecological integrity by genetic algorithm to quantitatively select operational indicators of ecological construction. Our study will contribute to deeply understanding the connotation and improving the science and validity of the ecological indicators system for ecological construction assessment and management applications.

Polarimetric RADARSAT-2 imagery for soil moisture retrieval in alpine areas

In this work, the polarimetric capability of RADARSAT-2 images is exploited in the aim of soil moisture content retrieval in Alpine meadows and pastures. Three feature extraction methods are investigated: the simple polarimetric intensity and phase processing, the H/A/α polarimetric decomposition, and the Independent Component Analysis (ICA). The features extracted according to these strategies were assessed for their capability to improve the soil moisture estimation by considering both quantitative performance on a set of reference samples and qualitative analysis of the corresponding output soil moisture content maps. The method proposed for the soil moisture estimation was based on the Support Vector Regression technique combined with an innovative multi-objective model selection strategy. The results indicated that the use of polarimetric features such as HH and HV channels improved the estimation of soil moisture content in the investigated mountain area, especially because the HV channel was able to disentangle the vegetation effect on the radar signal. From the preliminary results presented in this paper, the use of the H/A/α polarimetric decomposition and the ICA technique seem to not determine a significant improvement in the soil moisture estimation.