Feature Weight Optimization Research Articles

A multi-scale feature fusion neural network for multi-class disease classification on the maize leaf images

Maize is a globally important cereal crop, however, maize leaf disease is one of the most common and devastating diseases that afflict it. Artificial intelligence methods face challenges in identifying and classifying maize leaf diseases due to variations in image quality, similarity among diseases, disease severity, limited dataset availability, and limited interpretability. To address these challenges, we propose a residual-based multi-scale network (MResNet) for classifying multi-type maize leaf diseases from maize images. MResNet consists of two residual subnets with different scales, enabling the model to detect diseases in maize leaf images at different scales. We further utilize a hybrid feature weight optimization method to optimize and fuse the feature mapping weights of two subnets. We validate MResNet on a maize leaf diseases dataset. MResNet achieves 97.45% accuracy. The performance of MResNet surpasses other state-of-the-art methods. Various experiments and two additional datasets confirm the generalization performance of our model. Furthermore, thermodynamic diagram analysis increases the interpretability of the model. This study provides technical support for the disease classification of agricultural plants.

Extractive multi-document text summarization using dolphin swarm optimization approach

Nowadays, extracting the desired information from internet source is a challenging task because of a large amount of information available on the internet. So, we propose a new extractive based approach for multi-document text summarization to extract useful information from multi-document. Initially, the redundant contents in the document create a single text file from the multiple text file document. The content coverage and non-redundancy features are achieved by Word Mover Distance (WMD) and Modified Normalized Google Distance (M-NGD) (WM) Hybrid Weight Method based similarity approaches. For feature weight optimization, we use the Dolphin swarm optimization (DSO) which is a metaheuristic approach. The proposed approach is tested under python with multiling 2013 dataset and the performances have been evaluated with ROUGE and AutoSummENG metrics. The investigational outcomes show that the proposed technique works well and very much effective for multi-document text summarization.

DABE: Differential evolution in analogy-based software development effort estimation

Abstract Several feature weight optimization techniques have been proposed for similarity functions in analogy-based estimation (ABE); however, no consensus regarding the method and settings suitable for producing accurate estimates has been reached. We investigate the effectiveness of differential evolution (DE) algorithm, for optimizing the feature weights of similarity functions of ABE by applying five successful mutation strategies. We have named this empirical analysis as DE in analogy-based software development effort estimation (DABE). We have conducted extensive simulation study on the PROMISE repository test suite to estimate the effectiveness of our proposed DABE technique. We find significant improvements in predictive performance of our DABE technique over ABE, particle swarm optimization-based feature weight optimization in ABE, genetic algorithm-based feature weight optimization in ABE, self-adaptive DE-based feature weight optimization ABE, adaptive differential evolution with optional external archive-based feature weight optimization ABE, functional link artificial neural network,artificial neural network with back propagation learning based software development effort estimation (SDEE), and radial basis function-based SDEE.

Feature Weight Optimization Mechanism for Email Spam Detection based on Two-step Clustering Algorithm and Logistic Regression Method

This research proposed an improved filtering spam technique for suspected emails, messages based on feature weight and the combination of two-step clustering and logistic regression algorithm. Unique, important features are used as the optimum input for a hybrid proposed approach. This study adopted a spam detector model based on distance measure and threshold value. The aim of this model was to study and select distinct features for email filtering using feature weight method as dimension reduction. Two-step clustering algorithm was used to generate a new feature called “Label” to cluster and differentiate the diversity emails and group them based on the inter samples similarity. Thereby the spam filtering process was simplified using the Logistic regression classifier in order to distinguish the hidden patterns of spam and non-spam emails. Experimental design was conducted based on the UCI spam dataset. The outcome of the findings shows that the results of the email filtering are promising compared to other modern spam filtering methods.

Co-Training Framework for Feature Weight Optimization of Statistic Machine Translation

PDF HTML阅读 XML下载 导出引用 引用提醒 一种面向统计机器翻译的协同权重训练方法 DOI: 10.3724/SP.J.1001.2012.04208 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: Co-Training Framework for Feature Weight Optimization of Statistic Machine Translation Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:分析了统计机器翻译中的特征权重的领域自适应问题,并针对该问题提出了协同的权重训练方法.该方法使用来自不同解码器的译文作为准参考译文,并将其加入到开发集中,使得特征权重的训练过程向测试集所在的领域倾斜.此外,提出了使用最小贝叶斯风险的系统融合方法来选择准参考译文,进一步提高了协同权重训练的性能.实验结果表明,使用最小贝叶斯风险系统融合的协同训练方法,可以在一定程度上解决特征权重的领域自适应问题,并显著地提高了在目标领域内机器翻译结果的质量. Abstract:In this paper, based on the investigation of domain adaptation for feature weight, the study proposes to use a co-training framework to handle domain adaptation for feature weight, i.e. The study uses the translation results from another heterogeneous decoder as pseudo references and adds them to the development data set for minimum error rate training to bias the feature weight to the domain of test data set. Furthermore, the study uses a minimum Bayes- Risk combination for pseudo reference selection, which can pick proper translation results from the translation candidates from both decoders to smooth the training process. Experimental results show that this co-training method with a minimum Bayes-Risk combination can yield significant improvements in target domain. 参考文献 相似文献 引证文献

Soft syntactic constraints for Arabic–English hierarchical phrase-based translation

In adding syntax to statistical machine translation, there is a tradeoff between taking advantage of linguistic analysis and allowing the model to exploit parallel training data with no linguistic analysis: translation quality versus coverage. A number of previous efforts have tackled this tradeoff by starting with a commitment to linguistically motivated analyses and then finding appropriate ways to soften that commitment. We present an approach that explores the tradeoff from the other direction, starting with a translation model learned directly from aligned parallel text, and then adding soft constituent-level constraints based on parses of the source language. We argue that in order for these constraints to improve translation, they must be fine-grained: the constraints should vary by constituent type, and by the type of match or mismatch with the parse. We also use a different feature weight optimization technique, capable of handling large amount of features, thus eliminating the bottleneck of feature selection. We obtain substantial improvements in performance for translation from Arabic to English.

Building Controllers for Tetris

This article has two purposes: a review on the problem of building a controller for the well-known video game Tetris, and a contribution on how to achieve the best performance. Key components of typical solutions include feature design and feature-weight optimization. We provide a list of all the features we could find in the literature and in implementations, and mention the methods that have been used for weight optimization. We also highlight the fact that performance measures for Tetris must be compared with great care, as (1) they have a rather large variance, and (2) subtle implementation choices can have a significant effect on the resulting scores. An immediate interest of this review is illustrated. Straightforwardly gathering ideas from different works may lead to new ideas. We show how we built a controller that outperforms the previously known best controllers. Finally, we briefly discuss how this implementation allowed us to win the Tetris-domain prize of the 2008 Reinforcement Learning Competition.