Relevant bibliographies by topics / Feature and model selection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Feature and model selection'

Author: Grafiati
Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "Feature and model selection"

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Huber, Florian, and Volker Steinhage. "Conditional Feature Selection: Evaluating Model Averaging When Selecting Features with Shapley Values." Geomatics 4, no. 3 (2024): 286–310. http://dx.doi.org/10.3390/geomatics4030016.

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In the field of geomatics, artificial intelligence (AI) and especially machine learning (ML) are rapidly transforming the field of geomatics with respect to collecting, managing, and analyzing spatial data. Feature selection as a building block in ML is crucial because it directly impacts the performance and predictive power of a model by selecting the most critical variables and eliminating the redundant and irrelevant ones. Random forests have now been used for decades and allow for building models with high accuracy. However, finding the most expressive features from the dataset by selecting the most important features within random forests is still a challenging question. The often-used internal Gini importances of random forests are based on the amount of training examples that are divided by a feature but fail to acknowledge the magnitude of change in the target variable, leading to suboptimal selections. Shapley values are an established and unified framework for feature attribution, i.e., specifying how much each feature in a trained ML model contributes to the predictions for a given instance. Previous studies highlight the effectiveness of Shapley values for feature selection in real-world applications, while other research emphasizes certain theoretical limitations. This study provides an application-driven discussion of Shapley values for feature selection by first proposing four necessary conditions for a successful feature selection with Shapley values that are extracted from a multitude of critical research in the field. Given these valuable conditions, Shapley value feature selection is nevertheless a model averaging procedure by definition, where unimportant features can alter the final selection. Therefore, we additionally present Conditional Feature Selection (CFS) as a novel algorithm for performing feature selection that mitigates this problem and use it to evaluate the impact of model averaging in several real-world examples, covering the use of ML in geomatics. The results of this study show Shapley values as a good measure for feature selection when compared with Gini feature importances on four real-world examples, improving the RMSE by 5% when averaged over selections of all possible subset sizes. An even better selection can be achieved by CFS, improving on the Gini selection by approximately 7.5% in terms of RMSE. For random forests, Shapley value calculation can be performed in polynomial time, offering an advantage over the exponential runtime of CFS, building a trade-off to the lost accuracy in feature selection due to model averaging.
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Wang, Jun, Yuanyuan Xu, Hengpeng Xu, Zhe Sun, Zhenglu Yang, and Jinmao Wei. "An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features." Applied Sciences 10, no. 22 (2020): 8093. http://dx.doi.org/10.3390/app10228093.

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Feature selection has devoted a consistently great amount of effort to dimension reduction for various machine learning tasks. Existing feature selection models focus on selecting the most discriminative features for learning targets. However, this strategy is weak in handling two kinds of features, that is, the irrelevant and redundant ones, which are collectively referred to as noisy features. These features may hamper the construction of optimal low-dimensional subspaces and compromise the learning performance of downstream tasks. In this study, we propose a novel multi-label feature selection approach by embedding label correlations (dubbed ELC) to address these issues. Particularly, we extract label correlations for reliable label space structures and employ them to steer feature selection. In this way, label and feature spaces can be expected to be consistent and noisy features can be effectively eliminated. An extensive experimental evaluation on public benchmarks validated the superiority of ELC.
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Wang, Gang, Yang Zhao, Jiasi Zhang, and Yongjie Ning. "A Novel End-To-End Feature Selection and Diagnosis Method for Rotating Machinery." Sensors 21, no. 6 (2021): 2056. http://dx.doi.org/10.3390/s21062056.

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Feature selection is to obtain effective features from data, also known as feature engineering. Traditional feature selection and predictive model learning are separated, and there is a problem of inconsistency of criteria. This paper presents an end-to-end feature selection and diagnosis method that organically unifies feature expression learning and machine prediction learning into one model. The algorithm first combines the prediction model to calculate the mean impact value (MIVs) of the feature and realizes primary feature selection for the prediction model by selecting the feature with a larger MIV. In order to take into account the performance of the feature itself, the within-class and between-class discriminant analysis (WBDA) method is proposed, and combined with the feature diversity strategy, the feature-oriented secondary selection is realized. Eventually, feature vectors obtained by two selections are classified using a multi-class support vector machine (SVM). Compared with the modified network variable selection algorithm (MIVs), the principal component analysis dimensionality reduction algorithm (PCA), variable selection based on compensative distance evaluation technology (CDET), and other algorithms, the proposed method MIVs-WBDA exhibits excellent classification accuracy owing to the fusion of feature selection and predictive model learning. According to the results of classification accuracy testing after dimensionality reduction on rotating machinery status, the MIVs-WBDA method has a 3% classification accuracy improvement under the low-dimensional feature set. The typical running time of this classification learning algorithm is less than 10 s, while using deep learning, its running time will be more than a few hours.
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M Hafidz Ariansyah, Esmi Nur Fitri, and Sri Winarno. "IMPROVING PERFORMANCE OF STUDENTS’ GRADE CLASSIFICATION MODEL USES NAÏVE BAYES GAUSSIAN TUNING MODEL AND FEATURE SELECTION." Jurnal Teknik Informatika (Jutif) 4, no. 3 (2023): 493–501. http://dx.doi.org/10.52436/1.jutif.2023.4.3.737.

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Student grades are a relevant variable for predicting student academic performance. In achieving good and quality student performance, it is necessary to analyze or evaluate the factors that influence student performance. When a educator can predict students' academic performance from the start, the educator can adjust the way of learning so that learning can run effectively. The purpose of this research is to study how it is applied to determine the interrelationships between variables and find out which variables have an effect, then use it as a feature selection technique. Then, researchers review the most popular classifier, Gaussian Naïve Bayes (GNB). Next, we survey the feature selection models and discuss the feature selection approach. In this study, researchers will classify student grades based on existing features to evaluate student performance, so it can guide educators in selecting learning methods and assist students in planning the learning process. The result is that applying Gaussian Naïve Bayes (GNB) without feature selection has a lower accuracy of 10.12% while using feature selection the accuracy increases to 10.12%.
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Asst., Professor Mohammad Salim Hamdard, and Professor Hedayatullah Lodin Asst. "Effect of Feature Selection on the Accuracy of Machine Learning Model." INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS 06, no. 09 (2023): 4460–66. https://doi.org/10.5281/zenodo.8379528.

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In real life data science problems, it’s almost rare that all the features in the dataset are useful for building a model. In machine learning, feature selection is the process of selecting a subset of relevant features or attributes for constructing a model. Removing irrelevant and redundant features and, selecting relevant features will improve the accuracy of a machine learning model. Furthermore, adding unnecessary variables to a model increases the overall complexity of the model. Our experiment indicates that the accuracy of a classification model is highly affected by the process of feature selection. We train three algorithms (K-Nearest Neighbors, Decision Tree, Multi-layer Perceptron) by selecting all the features and we got accuracies 49%, 84% and 71% accordingly. After doing some feature selection without any logical changes in models code the accuracy scores jumped to 82%, 86% and 78% accordingly which is quite impressive.
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Guru, D. S., N. Vinay Kumar, and Mahamad Suhil. "Feature Selection of Interval Valued Data Through Interval K-Means Clustering." International Journal of Computer Vision and Image Processing 7, no. 2 (2017): 64–80. http://dx.doi.org/10.4018/ijcvip.2017040105.

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This paper introduces a novel feature selection model for supervised interval valued data based on interval K-Means clustering. The proposed model explores two kinds of feature selection through feature clustering viz., class independent feature selection and class dependent feature selection. The former one clusters the features spread across all the samples belonging to all the classes, whereas the latter one clusters the features spread across only the samples belonging to the respective classes. Both feature selection models are demonstrated to explore the generosity of clustering in selecting the interval valued features. For clustering, the kernel of the K-means clustering has been altered to operate on interval valued data. For experimentation purpose four standard benchmarking datasets and three symbolic classifiers have been used. To corroborate the effectiveness of the proposed model, a comparative analysis against the state-of-the-art models is given and results show the superiority of the proposed model.
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K, Bhuvaneswari. "Filter Based Sentiment Feature Selection Using Back Propagation Deep Learning." Journal of Computer Sciences and Informatics 2, no. 1 (2025): 15. https://doi.org/10.5455/jcsi.20241216054507.

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Aim: The proposed Filter Based Sentiment Feature Selection (FBSFS) model focuses on to improve the performance of Sentiment Learning (SL) by selecting the most relevant sentiment features from text reviews using feature selection methods at document level. Method: Sentiment Learning is applied at the document level for classifying text reviews into two categories either positive or negative. The key sentiment features adjectives (ADJ), adverbs (ADV), and verbs (VRB) which are essential for sentiment analysis, are extracted from text document using the WordNet dictionary. Feature selection is performed by applying four different algorithms: Information Gain, Correlation, Gini Index, and Chi-Square. These algorithms help identify the most significant features that contribute to sentiment classification. The selected features are then fed into a Back Propagation Deep Learning (BPDL) classification model for sentiment analysis. Result: The experimental findings show that the proposed model achieved higher accuracy of 91.15% using Correlation feature selection. This accuracy signifies the effectiveness of the proposed model in classifying text reviews, outperforming other methods in terms of sentiment feature selection and classification. Conclusion: The proposed model enhances the performance of sentiment learning by selecting the most relevant sentiment features, particularly those extracted from adjectives, adverbs, and verbs, and combining them with BPDL. The FBSFS model as a robust tool for sentiment classification.
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Sholeh, Muhammad, Uning Lestari, and Dina Andayati. "Comparison of Feature Selection with Information Gain Method in Decision Tree, Regression Logistic and Random Forest Algorithms." Journal of Applied Business and Technology 5, no. 3 (2024): 146–53. https://doi.org/10.35145/jabt.v5i3.155.

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One of the approaches that can be done is to perform feature selection. Feature selection is done by identifying the most informative features and not using features that do not directly contribute to the target feature. The purpose of feature selection is to increase the accuracy of the model. The research was conducted by comparing the performance of the model by comparing the accuracy results of the model without any feature selection with the model that has done feature selection. The process is done by comparing the accuracy results with decision tree, random forest and SVM algorithms. In the research method of feature selection on science data, the steps include understanding the domain and dataset, exploratory analysis, data cleaning, measuring feature relevance with criteria such as Information Gain, and feature ranking. The results are evaluated and validated using model performance metrics before and after feature selection. This process ensures selection of relevant features, improving accuracy. The research process used the Lung Cancer Prediction datasheet which consists of 306 rows and 16 attributes. The results show that feature selection can improve the performance of the classification model by reducing features that do not contribute to the target. Comparison results using decision tree, Regression Logistic and random forest classification model algorithms and feature selection resulted in a high accuracy value of 0.968 in the Regression Logistic algorithm with a feature selection of 5.
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Li, Deyang. "Feature Selection Based on Stock Prediction Model." Journal of Physics: Conference Series 2386, no. 1 (2022): 012021. http://dx.doi.org/10.1088/1742-6596/2386/1/012021.

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Abstract Stocks as an important part of financial investment are becoming more and more popular, and they have higher rates of both returns and risks. Making a prediction for the stock can reduce its risk and help people gain returns. So far, the traditional machine learning model is still unable to achieve ideal accuracy. The paper is devoted to analyzing the input features to improve the performance of stock forecasting models. Aiming at the problem that the traditional stock prediction algorithms produce different accuracy for the models constructed by different input features, the paper, through a method of establishing a long-term memory (LSTM) model, predicts the stock. The Shixia Technology Stock’s history data includes 5 features as the dataset and chooses the different feature as the input. In the experiment in this paper, different results are produced by subtracting one different input feature at a time. Finally, the model’s predictions were compared with each other by the R_square and RMSE, and the analysis revealed which feature could have a greater impact on the stock prediction. The paper finds that the different input features have different influences on the model fitting effect and the prediction accuracy on stock forecasting based on the same neural network model.
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Karthiga, B., Sathya Selvaraj Sinnasamy, V. C. Bharathi, K. Azarudeen, and P. Sherubha. "Design of a Classifier model for Heart Disease Prediction using normalized graph model." Salud, Ciencia y Tecnología - Serie de Conferencias 3 (March 23, 2024): 653. http://dx.doi.org/10.56294/sctconf2024653.

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Heart disease is an illness that influences enormous people worldwide. Particularly in cardiology, heart disease diagnosis and treatment need to happen quickly and precisely. Here, a machine learning-based (ML) approach is anticipated for diagnosing a cardiac disease that is both effective and accurate. The system was developed using standard feature selection algorithms for removing unnecessary and redundant features. Here, a novel normalized graph model (n-GM) is used for prediction. To address the issue of feature selection, this work considers the significant information feature selection approach. To improve classification accuracy and shorten the time it takes to process classifications, feature selection techniques are utilized. Furthermore, the hyper-parameters and learning techniques for model evaluation have been accomplished using cross-validation. The performance is evaluated with various metrics. The performance is evaluated on the features chosen via features representation. The outcomes demonstrate that the suggested n-GM gives 98% accuracy for modeling an intelligent system to detect heart disease using a classifier support vector machine
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Dissertations / Theses on the topic "Feature and model selection"

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Gustafsson, Robin. "Ordering Classifier Chains using filter model feature selection techniques." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14817.

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Context: Multi-label classification concerns classification with multi-dimensional output. The Classifier Chain breaks the multi-label problem into multiple binary classification problems, chaining the classifiers to exploit dependencies between labels. Consequently, its performance is influenced by the chain's order. Approaches to finding advantageous chain orders have been proposed, though they are typically costly. Objectives: This study explored the use of filter model feature selection techniques to order Classifier Chains. It examined how feature selection techniques can be adapted to evaluate label dependence, how such information can be used to select a chain order and how this affects the classifier's performance and execution time. Methods: An experiment was performed to evaluate the proposed approach. The two proposed algorithms, Forward-Oriented Chain Selection (FOCS) and Backward-Oriented Chain Selection (BOCS), were tested with three different feature evaluators. 10-fold cross-validation was performed on ten benchmark datasets. Performance was measured in accuracy, 0/1 subset accuracy and Hamming loss. Execution time was measured during chain selection, classifier training and testing. Results: Both proposed algorithms led to improved accuracy and 0/1 subset accuracy (Friedman & Hochberg, p < 0.05). FOCS also improved the Hamming loss while BOCS did not. Measured effect sizes ranged from 0.20 to 1.85 percentage points. Execution time was increased by less than 3 % in most cases. Conclusions: The results showed that the proposed approach can improve the Classifier Chain's performance at a low cost. The improvements appear similar to comparable techniques in magnitude but at a lower cost. It shows that feature selection techniques can be applied to chain ordering, demonstrates the viability of the approach and establishes FOCS and BOCS as alternatives worthy of further consideration.
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McCann, Michael. "A feature selection design model for business improvement in semiconductor process engineering." Thesis, Ulster University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538949.

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Maboudi, Afkham Heydar. "Improving Image Classification Performance using Joint Feature Selection." Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-144896.

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In this thesis, we focus on the problem of image classification and investigate how its performance can be systematically improved. Improving the performance of different computer vision methods has been the subject of many studies. While different studies take different approaches to achieve this improvement, in this thesis we address this problem by investigating the relevance of the statistics collected from the image. We propose a framework for gradually improving the quality of an already existing image descriptor. In our studies, we employ a descriptor which is composed the response of a series of discriminative components for summarizing each image. As we will show, this descriptor has an ideal form in which all categories become linearly separable. While, reaching this form is not possible, we will argue how by replacing a small fraction of these components, it is possible to obtain a descriptor which is, on average, closer to this ideal form. To do so, we initially identify which components do not contribute to the quality of the descriptor and replace them with more robust components. As we will show, this replacement has a positive effect on the quality of the descriptor. While there are many ways of obtaining more robust components, we introduce a joint feature selection problem to obtain image features that retains class discriminative properties while simultaneously generalising between within class variations. Our approach is based on the concept of a joint feature where several small features are combined in a spatial structure. The proposed framework automatically learns the structure of the joint constellations in a class dependent manner improving the generalisation and discrimination capabilities of the local descriptor while still retaining a low-dimensional representations. The joint feature selection problem discussed in this thesis belongs to a specific class of latent variable models that assumes each labeled sample is associated with a set of different features, with no prior knowledge of which feature is the most relevant feature to be used. Deformable-Part Models (DPM) can be seen as good examples of such models. These models are usually considered to be expensive to train and very sensitive to the initialization. Here, we focus on the learning of such models by introducing a topological framework and show how it is possible to both reduce the learning complexity and produce more robust decision boundaries. We will also argue how our framework can be used for producing robust decision boundaries without exploiting the dataset bias or relying on accurate annotations. To examine the hypothesis of this thesis, we evaluate different parts of our framework on several challenging datasets and demonstrate how our framework is capable of gradually improving the performance of image classification by collecting more robust statistics from the image and improving the quality of the descriptor.<br><p>QC 20140506</p>
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Li, Qi. "Application of Improved Feature Selection Algorithm in SVM Based Market Trend Prediction Model." Thesis, Portland State University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10979352.

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<p> In this study, a <b>Prediction Accuracy Based Hill Climbing Feature Selection Algorithm</b> <b>(AHCFS)</b> is created and compared with an <b>Error Rate Based Sequential Feature Selection Algorithm</b> <b> (ERFS)</b> which is an existing Matlab algorithm. The goal of the study is to create a new piece of an algorithm that has potential to outperform the existing Matlab sequential feature selection algorithm in predicting the movement of S&amp;P 500 (</p><p>GSPC) prices under certain circumstances. The twoalgorithms are tested based on historical data of </p><p>GSPC, and <b>SupportVector Machine</b> <b>(SVM)</b> is employed by both as the classifier. A prediction without feature selection algorithm implemented is carried out and used as a baseline for comparison between the two algorithms. The prediction horizon set in this study for both algorithms varies from one to 60 days. The study results show that AHCFS reaches higher prediction accuracy than ERFS in the majority of the cases.</p><p>
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Butko, Taras. "Feature selection for multimodal: acoustic event detection." Doctoral thesis, Universitat Politècnica de Catalunya, 2011. http://hdl.handle.net/10803/32176.

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The detection of the Acoustic Events (AEs) naturally produced in a meeting room may help to describe the human and social activity. The automatic description of interactions between humans and environment can be useful for providing: implicit assistance to the people inside the room, context-aware and content-aware information requiring a minimum of human attention or interruptions, support for high-level analysis of the underlying acoustic scene, etc. On the other hand, the recent fast growth of available audio or audiovisual content strongly demands tools for analyzing, indexing, searching and retrieving the available documents. Given an audio document, the first processing step usually is audio segmentation (AS), i.e. the partitioning of the input audio stream into acoustically homogeneous regions which are labelled according to a predefined broad set of classes like speech, music, noise, etc. Acoustic event detection (AED) is the objective of this thesis work. A variety of features coming not only from audio but also from the video modality is proposed to deal with that detection problem in meeting-room and broadcast news domains. Two basic detection approaches are investigated in this work: a joint segmentation and classification using Hidden Markov Models (HMMs) with Gaussian Mixture Densities (GMMs), and a detection-by-classification approach using discriminative Support Vector Machines (SVMs). For the first case, a fast one-pass-training feature selection algorithm is developed in this thesis to select, for each AE class, the subset of multimodal features that shows the best detection rate. AED in meeting-room environments aims at processing the signals collected by distant microphones and video cameras in order to obtain the temporal sequence of (possibly overlapped) AEs that have been produced in the room. When applied to interactive seminars with a certain degree of spontaneity, the detection of acoustic events from only the audio modality alone shows a large amount of errors, which is mostly due to the temporal overlaps of sounds. This thesis includes several novelties regarding the task of multimodal AED. Firstly, the use of video features. Since in the video modality the acoustic sources do not overlap (except for occlusions), the proposed features improve AED in such rather spontaneous scenario recordings. Secondly, the inclusion of acoustic localization features, which, in combination with the usual spectro-temporal audio features, yield a further improvement in recognition rate. Thirdly, the comparison of feature-level and decision-level fusion strategies for the combination of audio and video modalities. In the later case, the system output scores are combined using two statistical approaches: weighted arithmetical mean and fuzzy integral. On the other hand, due to the scarcity of annotated multimodal data, and, in particular, of data with temporal sound overlaps, a new multimodal database with a rich variety of meeting-room AEs has been recorded and manually annotated, and it has been made publicly available for research purposes.<br>La detecció d'esdeveniments acústics (Acoustic Events -AEs-) que es produeixen naturalment en una sala de reunions pot ajudar a descriure l'activitat humana i social. La descripció automàtica de les interaccions entre els éssers humans i l'entorn pot ser útil per a proporcionar: ajuda implícita a la gent dins de la sala, informació sensible al context i al contingut sense requerir gaire atenció humana ni interrupcions, suport per a l'anàlisi d'alt nivell de l'escena acústica, etc. La detecció i la descripció d'activitat és una funcionalitat clau de les interfícies perceptives que treballen en entorns de comunicació humana com sales de reunions. D'altra banda, el recent creixement ràpid del contingut audiovisual disponible requereix l'existència d'eines per a l'anàlisi, indexació, cerca i recuperació dels documents existents. Donat un document d'àudio, el primer pas de processament acostuma a ser la seva segmentació (Audio Segmentation (AS)), és a dir, la partició de la seqüència d'entrada d'àudio en regions acústiques homogènies que s'etiqueten d'acord amb un conjunt predefinit de classes com parla, música, soroll, etc. De fet, l'AS pot ser vist com un cas particular de la detecció d’esdeveniments acústics, i així es fa en aquesta tesi. La detecció d’esdeveniments acústics (Acoustic Event Detection (AED)) és un dels objectius d'aquesta tesi. Es proposa tot una varietat de característiques que provenen no només de l'àudio, sinó també de la modalitat de vídeo, per fer front al problema de la detecció en dominis de sala de reunions i de difusió de notícies. En aquest treball s'investiguen dos enfocaments bàsics de detecció: 1) la realització conjunta de segmentació i classificació utilitzant models de Markov ocults (Hidden Markov Models (HMMs)) amb models de barreges de gaussianes (Gaussian Mixture Models (GMMs)), i 2) la detecció per classificació utilitzant màquines de vectors suport (Support Vector Machines (SVM)) discriminatives. Per al primer cas, en aquesta tesi es desenvolupa un algorisme de selecció de característiques ràpid d'un sol pas per tal de seleccionar, per a cada AE, el subconjunt de característiques multimodals que aconsegueix la millor taxa de detecció. L'AED en entorns de sales de reunió té com a objectiu processar els senyals recollits per micròfons distants i càmeres de vídeo per tal d'obtenir la seqüència temporal dels (possiblement superposats) esdeveniments acústics que s'han produït a la sala. Quan s'aplica als seminaris interactius amb un cert grau d'espontaneïtat, la detecció d'esdeveniments acústics a partir de només la modalitat d'àudio mostra una gran quantitat d'errors, que és sobretot a causa de la superposició temporal dels sons. Aquesta tesi inclou diverses contribucions pel que fa a la tasca d'AED multimodal. En primer lloc, l'ús de característiques de vídeo. Ja que en la modalitat de vídeo les fonts acústiques no se superposen (exceptuant les oclusions), les característiques proposades Resum iv milloren la detecció en els enregistraments en escenaris de caire espontani. En segon lloc, la inclusió de característiques de localització acústica, que, en combinació amb les característiques habituals d'àudio espectrotemporals, signifiquen nova millora en la taxa de reconeixement. En tercer lloc, la comparació d'estratègies de fusió a nivell de característiques i a nivell de decisions, per a la utilització combinada de les modalitats d'àudio i vídeo. En el darrer cas, les puntuacions de sortida del sistema es combinen fent ús de dos mètodes estadístics: la mitjana aritmètica ponderada i la integral difusa. D'altra banda, a causa de l'escassetat de dades multimodals anotades, i, en particular, de dades amb superposició temporal de sons, s'ha gravat i anotat manualment una nova base de dades multimodal amb una rica varietat d'AEs de sala de reunions, i s'ha posat a disposició pública per a finalitats d'investigació. Per a la segmentació d'àudio en el domini de difusió de notícies, es proposa una arquitectura jeràrquica de sistema, que agrupa apropiadament un conjunt de detectors, cada un dels quals correspon a una de les classes acústiques d'interès. S'han desenvolupat dos sistemes diferents de SA per a dues bases de dades de difusió de notícies: la primera correspon a gravacions d'àudio del programa de debat Àgora del canal de televisió català TV3, i el segon inclou diversos segments d'àudio del canal de televisió català 3/24 de difusió de notícies. La sortida del primer sistema es va utilitzar com a primera etapa dels sistemes de traducció automàtica i de subtitulat del projecte Tecnoparla, un projecte finançat pel govern de la Generalitat en el que es desenvoluparen diverses tecnologies de la parla per extreure tota la informació possible del senyal d'àudio. El segon sistema d'AS, que és un sistema de detecció jeràrquica basat en HMM-GMM amb selecció de característiques, ha obtingut resultats competitius en l'avaluació de segmentació d'àudio Albayzín2010. Per acabar, val la pena esmentar alguns resultats col·laterals d’aquesta tesi. L’autor ha sigut responsable de l'organització de l'avaluació de sistemes de segmentació d'àudio dins de la campanya Albayzín-2010 abans esmentada. S'han especificat les classes d’esdeveniments, les bases de dades, la mètrica i els protocols d'avaluació utilitzats, i s'ha realitzat una anàlisi posterior dels sistemes i els resultats presentats pels vuit grups de recerca participants, provinents d'universitats espanyoles i portugueses. A més a més, s'ha implementat en la sala multimodal de la UPC un sistema de detecció d'esdeveniments acústics per a dues fonts simultànies, basat en HMM-GMM, i funcionant en temps real, per finalitats de test i demostració.
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Tarca, Adi-Laurentiu. "Neural networks in multiphase reactors data mining: feature selection, prior knowledge, and model design." Thesis, Université Laval, 2004. http://www.theses.ulaval.ca/2004/21673/21673.pdf.

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Les réseaux de neurones artificiels (RNA) suscitent toujours un vif intérêt dans la plupart des domaines d’ingénierie non seulement pour leur attirante « capacité d’apprentissage » mais aussi pour leur flexibilité et leur bonne performance, par rapport aux approches classiques. Les RNA sont capables «d’approximer» des relations complexes et non linéaires entre un vecteur de variables d’entrées x et une sortie y. Dans le contexte des réacteurs multiphasiques le potentiel des RNA est élevé car la modélisation via la résolution des équations d’écoulement est presque impossible pour les systèmes gaz-liquide-solide. L’utilisation des RNA dans les approches de régression et de classification rencontre cependant certaines difficultés. Un premier problème, général à tous les types de modélisation empirique, est celui de la sélection des variables explicatives qui consiste à décider quel sous-ensemble xs ⊂ x des variables indépendantes doit être retenu pour former les entrées du modèle. Les autres difficultés à surmonter, plus spécifiques aux RNA, sont : le sur-apprentissage, l’ambiguïté dans l’identification de l’architecture et des paramètres des RNA et le manque de compréhension phénoménologique du modèle résultant. Ce travail se concentre principalement sur trois problématiques dans l’utilisation des RNA: i) la sélection des variables, ii) l’utilisation de la connaissance apriori, et iii) le design du modèle. La sélection des variables, dans le contexte de la régression avec des groupes adimensionnels, a été menée avec les algorithmes génétiques. Dans le contexte de la classification, cette sélection a été faite avec des méthodes séquentielles. Les types de connaissance a priori que nous avons insérés dans le processus de construction des RNA sont : i) la monotonie et la concavité pour la régression, ii) la connectivité des classes et des coûts non égaux associés aux différentes erreurs, pour la classification. Les méthodologies développées dans ce travail ont permis de construire plusieurs modèles neuronaux fiables pour les prédictions de la rétention liquide et de la perte de charge dans les colonnes garnies à contre-courant ainsi que pour la prédiction des régimes d’écoulement dans les colonnes garnies à co-courant.<br>Artificial neural networks (ANN) have recently gained enormous popularity in many engineering fields, not only for their appealing “learning ability,” but also for their versatility and superior performance with respect to classical approaches. Without supposing a particular equational form, ANNs mimic complex nonlinear relationships that might exist between an input feature vector x and a dependent (output) variable y. In the context of multiphase reactors the potential of neural networks is high as the modeling by resolution of first principle equations to forecast sought key hydrodynamics and transfer characteristics is intractable. The general-purpose applicability of neural networks in regression and classification, however, poses some subsidiary difficulties that can make their use inappropriate for certain modeling problems. Some of these problems are general to any empirical modeling technique, including the feature selection step, in which one has to decide which subset xs ⊂ x should constitute the inputs (regressors) of the model. Other weaknesses specific to the neural networks are overfitting, model design ambiguity (architecture and parameters identification), and the lack of interpretability of resulting models. This work addresses three issues in the application of neural networks: i) feature selection ii) prior knowledge matching within the models (to answer to some extent the overfitting and interpretability issues), and iii) the model design. Feature selection was conducted with genetic algorithms (yet another companion from artificial intelligence area), which allowed identification of good combinations of dimensionless inputs to use in regression ANNs, or with sequential methods in a classification context. The type of a priori knowledge we wanted the resulting ANN models to match was the monotonicity and/or concavity in regression or class connectivity and different misclassification costs in classification. Even the purpose of the study was rather methodological; some resulting ANN models might be considered contributions per se. These models-- direct proofs for the underlying methodologies-- are useful for predicting liquid hold-up and pressure drop in counter-current packed beds and flow regime type in trickle beds.
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Liang, Wen. "Integrated feature, neighbourhood, and model optimization for personalised modelling and knowledge discovery." Click here to access this resource online, 2009. http://hdl.handle.net/10292/749.

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“Machine learning is the process of discovering and interpreting meaningful information, such as new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (Larose, 2005). From my understanding, machine learning is a process of using different analysis techniques to observe previously unknown, potentially meaningful information, and discover strong patterns and relationships from a large dataset. Professor Kasabov (2007b) classified computational models into three categories (e.g. global, local, and personalised) which have been widespread and used in the areas of data analysis and decision support in general, and in the areas of medicine and bioinformatics in particular. Most recently, the concept of personalised modelling has been widely applied to various disciplines such as personalised medicine, personalised drug design for known diseases (e.g. cancer, diabetes, brain disease, etc.) as well as for other modelling problems in ecology, business, finance, crime prevention, and so on. The philosophy behind the personalised modelling approach is that every person is different from others, thus he/she will benefit from having a personalised model and treatment. However, personalised modelling is not without issues, such as defining the correct number of neighbours or defining an appropriate number of features. As a result, the principal goal of this research is to study and address these issues and to create a novel framework and system for personalised modelling. The framework would allow users to select and optimise the most important features and nearest neighbours for a new input sample in relation to a certain problem based on a weighted variable distance measure in order to obtain more precise prognostic accuracy and personalised knowledge, when compared with global modelling and local modelling approaches.
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Watts-Willis, Tristan A. "Autonomous model selection for surface classification via unmanned aerial vehicle." Scholarly Commons, 2017. https://scholarlycommons.pacific.edu/uop_etds/224.

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In the pursuit of research in remote areas, robots may be employed to deploy sensor networks. These robots need a method of classifying a surface to determine if it is a suitable installation site. Developing surface classification models manually requires significant time and detracts from the goal of automating systems. We create a system that automatically collects the data using an Unmanned Aerial Vehicle (UAV), extracts features, trains a large number of classifiers, selects the best classifier, and programs the UAV with that classifier. We design this system with user configurable parameters for choosing a high accuracy, efficient classifier. In support of this system, we also develop an algorithm for evaluating the effectiveness of individual features as indicators of the variable of interest. Motivating our work is a prior project that manually developed a surface classifier using an accelerometer; we replicate those results with our new automated system and improve on those results, providing a four-surface classifier with a 75% classification rate and a hard/soft classifier with a 100% classification rate. We further verify our system through a field experiment that collects and classifies new data, proving its end-to-end functionality. The general form of our system provides a valuable tool for automation of classifier creation and is released as an open-source tool.
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Algarni, Abdulmohsen. "Relevance feature discovery for text analysis." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48230/1/Abdulmohsen_Algarni_Thesis.pdf.

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It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term- based ones in describing user preferences, but many experiments do not support this hypothesis. This research presents a promising method, Relevance Feature Discovery (RFD), for solving this challenging issue. It discovers both positive and negative patterns in text documents as high-level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the high-level features. The thesis also introduces an adaptive model (called ARFD) to enhance the exibility of using RFD in adaptive environment. ARFD automatically updates the system's knowledge based on a sliding window over new incoming feedback documents. It can efficiently decide which incoming documents can bring in new knowledge into the system. Substantial experiments using the proposed models on Reuters Corpus Volume 1 and TREC topics show that the proposed models significantly outperform both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and other pattern-based methods.
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Alrjebi, Mustafa M. M. "Robust Face Recognition via Multi-channel Models and Feature Selection." Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/66074.

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This thesis proposes various novel approaches to improve the performance of face recognition via using multi channels for face image representation. Methods proposed in this thesis are designed to solve different face recognition problems including Opened Mouth, Occlusions, Illumination variations, and Pose variations. All proposed methods have achieved significant improvements over state of the art methods.
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Books on the topic "Feature and model selection"

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Hristea, Florentina T. The Naïve Bayes Model for Unsupervised Word Sense Disambiguation: Aspects Concerning Feature Selection. Springer Berlin Heidelberg, 2013.

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Jensen, Richard. Computational intelligence and feature selection: Rough and fuzzy approaches. Wiley, 2008.

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Liu, Huan, and Hiroshi Motoda, eds. Feature Extraction, Construction and Selection. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5725-8.

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Cakmakov, Dusan. Feature selection for pattern recognition. Informa, 2002.

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missing], [name. Model selection. Institute of Mathematical Statistics, 2003.

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W, Zucchini, ed. Model selection. Wiley, 1986.

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Saunders, Craig, Marko Grobelnik, Steve Gunn, and John Shawe-Taylor, eds. Subspace, Latent Structure and Feature Selection. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752790.

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Bolón-Canedo, Verónica, Noelia Sánchez-Maroño, and Amparo Alonso-Betanzos. Feature Selection for High-Dimensional Data. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21858-8.

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Wan, Cen. Hierarchical Feature Selection for Knowledge Discovery. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-97919-9.

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1958-, Liu Huan, ed. Spectral feature selection for data mining. CRC Press, 2012.

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Book chapters on the topic "Feature and model selection"

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Felfernig, Alexander, Andreas Falkner, and David Benavides. "Interacting with Feature Model Configurators." In Feature Models. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-61874-1_4.

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AbstractIn this chapter, we discuss different AI techniques that can be applied to support interactive FM configuration scenarios.We have in mind situations where the user of a FM configurator is in the need of support, for example, in terms of requiring recommendations and related explanations for feature inclusions or exclusions or recommendations of how to get out of an inconsistent situation. We show how to support feature selection on the basis of recommendation technologies and also show how to apply the concepts of conflict detection and model-based diagnosis to support users in inconsistent situations as well as in the context of reconfiguration.
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Savić, Miloš, Vladimir Kurbalija, Mirjana Ivanović, and Zoran Bosnić. "A Feature Selection Method Based on Feature Correlation Networks." In Model and Data Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66854-3_19.

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Hristea, Florentina T. "Semantic WordNet-Based Feature Selection." In The Naïve Bayes Model for Unsupervised Word Sense Disambiguation. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33693-5_3.

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Hristea, Florentina T. "Syntactic Dependency-Based Feature Selection." In The Naïve Bayes Model for Unsupervised Word Sense Disambiguation. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33693-5_4.

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Guo, Gongde, Daniel Neagu, and Mark T. D. Cronin. "Using kNN Model for Automatic Feature Selection." In Pattern Recognition and Data Mining. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_44.

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Khtoom, Asma’a, and Mohammad Wedyan. "Feature Selection Models for Data Classification: Wrapper Model vs Filter Model." In Learning and Analytics in Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38501-9_25.

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Wang, Hongyan, and Jinwen Ma. "Simultaneous Model Selection and Feature Selection via BYY Harmony Learning." In Advances in Neural Networks – ISNN 2011. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21090-7_6.

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Jung, Tobias, and Peter Stone. "Feature Selection for Value Function Approximation Using Bayesian Model Selection." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04180-8_60.

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Purpura, Alberto, Karolina Buchner, Gianmaria Silvello, and Gian Antonio Susto. "Neural Feature Selection for Learning to Rank." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72240-1_34.

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AbstractLEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine learning models are employed to rank a set of items. In the past few years, neural LETOR approaches have become a competitive alternative to traditional ones like LambdaMART. However, neural architectures performance grew proportionally to their complexity and size. This can be an obstacle for their adoption in large-scale search systems where a model size impacts latency and update time. For this reason, we propose an architecture-agnostic approach based on a neural LETOR model to reduce the size of its input by up to 60% without affecting the system performance. This approach also allows to reduce a LETOR model complexity and, therefore, its training and inference time up to 50%.
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Pallavi, Smita, Akshay Kumar, and Utkarsh Mohan. "Feature Subset Selection Using IULDA Model for Prediction." In Nanoelectronics, Circuits and Communication Systems. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0776-8_18.

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Conference papers on the topic "Feature and model selection"

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Mai, Huy, and Xintao Wu. "On Prediction Feature Assignment in the Heckman Selection Model." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650475.

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Tabassoum, Nafiza, Farsha Bindu, Sanjida Sheikh, Raqeebir Rab, Abderrahmane Leshob, and Tamima Binte Wahab. "Multiclass Feature Selection Model for Adversarial Attacks in IoT Environment." In 2024 IEEE International Conference on e-Business Engineering (ICEBE). IEEE, 2024. https://doi.org/10.1109/icebe62490.2024.00017.

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Pérez-Piqueras, Víctor, Pablo Bermejo López, and José Gámez. "Agile Effort Estimation Improved by Feature Selection and Model Explainability." In 20th International Conference on Evaluation of Novel Approaches to Software Engineering. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013229800003928.

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Fayola, Cindy, Nicholas Lienardi, Wannie Tania, and Mediana Aryuni. "Implementation of Feature Selection in Online Learning Performance Prediction Model." In 2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT). IEEE, 2024. https://doi.org/10.1109/comnetsat63286.2024.10862975.

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Wang, Juanyan, and Mustafa Bilgic. "Context-Aware Feature Selection and Classification." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/480.

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We propose a joint model that performs instance-level feature selection and classification. For a given case, the joint model first skims the full feature vector, decides which features are relevant for that case, and makes a classification decision using only the selected features, resulting in compact, interpretable, and case-specific classification decisions. Because the selected features depend on the case at hand, we refer to this approach as context-aware feature selection and classification. The model can be trained on instances that are annotated by experts with both class labels and instance-level feature selections, so it can select instance-level features that humans would use. Experiments on several datasets demonstrate that the proposed model outperforms eight baselines on a combined classification and feature selection measure, and is able to better emulate the ground-truth instance-level feature selections. The supplementary materials are available at https://github.com/IIT-ML/IJCAI23-CFSC.
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Bai, Zilong, Hoa Nguyen, and Ian Davidson. "Block Model Guided Unsupervised Feature Selection." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2020. http://dx.doi.org/10.1145/3394486.3403173.

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Woznica, Adam, Phong Nguyen, and Alexandros Kalousis. "Model mining for robust feature selection." In the 18th ACM SIGKDD international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2339530.2339674.

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Alrajeh, Abdullah, and Mahesan Niranjan. "Bayesian Reordering Model with Feature Selection." In Proceedings of the Ninth Workshop on Statistical Machine Translation. Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/w14-3361.

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Yousef, Malik, Jens Allmer, and Waleed Khalifa. "Feature Selection for MicroRNA Target Prediction - Comparison of One-Class Feature Selection Methodologies." In 7th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005701602160225.

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Hongpeng, Tian, and Jiang Jia. "Sentence Embedding Model Based on Feature Selection." In 2020 International Conference on Computer Engineering and Application (ICCEA). IEEE, 2020. http://dx.doi.org/10.1109/iccea50009.2020.00144.

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Reports on the topic "Feature and model selection"

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Li, Qi. Application of Improved Feature Selection Algorithm in SVM Based Market Trend Prediction Model. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6614.

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Zhang, Yongping, Wen Cheng, and Xudong Jia. Enhancement of Multimodal Traffic Safety in High-Quality Transit Areas. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1920.

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Numerous extant studies are dedicated to enhancing the safety of active transportation modes, but very few studies are devoted to safety analysis surrounding transit stations, which serve as an important modal interface for pedestrians and bicyclists. This study bridges the gap by developing joint models based on the multivariate conditionally autoregressive (MCAR) priors with a distance-oriented neighboring weight matrix. For this purpose, transit-station-centered data in Los Angeles County were used for model development. Feature selection relying on both random forest and correlation analyses was employed, which leads to different covariate inputs to each of the two jointed models, resulting in increased model flexibility. Utilizing an Integrated Nested Laplace Approximation (INLA) algorithm and various evaluation criteria, the results demonstrate that models with a correlation effect between pedestrians and bicyclists perform much better than the models without such an effect. The joint models also aid in identifying significant covariates contributing to the safety of each of the two active transportation modes. The research results can furnish transportation professionals with additional insights to create safer access to transit and thus promote active transportation.
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Chung, Steve, Jaymin Kwon, and Yushin Ahn. Forecasting Commercial Vehicle Miles Traveled (VMT) in Urban California Areas. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2315.

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This study investigates commercial truck vehicle miles traveled (VMT) across six diverse California counties from 2000 to 2020. The counties—Imperial, Los Angeles, Riverside, San Bernardino, San Diego, and San Francisco—represent a broad spectrum of California’s demographics, economies, and landscapes. Using a rich dataset spanning demographics, economics, and pollution variables, we aim to understand the factors influencing commercial VMT. We first visually represent the geographic distribution of the counties, highlighting their unique characteristics. Linear regression models, particularly the least absolute shrinkage and selection operator (LASSO) and elastic net regressions are employed to identify key predictors of total commercial VMT. LASSO regression emphasizes feature selection, revealing vehicle population and fuel consumption as significant predictors in most counties. Elastic net regression, which balances feature selection and multicollinearity, expands the list of predictors to include variables like the number of trips, CO2 emissions, and PM2.5 pollution. Overall, the findings suggest that economic factors, such as fuel consumption and vehicle population, significantly impact the total commercial VMT across the counties. Pollution variables, specifically CO2 and PM2.5, also play a role. These insights underscore the need for nuanced transportation and environmental policies, especially in the face of economic fluctuations, to manage commercial truck VMT effectively and sustainably. Methodology using both LASSO and elastic net regression provides a robust framework for understanding these complex relationships in commercial transportation behavior.
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Choquette, Gary. PR-000-23COMP-R02 Unattended Facility Operation. Pipeline Research Council International, Inc. (PRCI), 2023. http://dx.doi.org/10.55274/r0000028.

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This compendium is a selection of past PRCI research that facilitates the operation of facilities in unattended modes. Reports were selected based on their final report abstracts and does not include further analysis or an additional technical review of the final report. Metadata is provided for each resource (when available) including the length of the report, its abstract, author(s), and company that performed the research. Links to each of the reports are provided to help facilitate quick access to reports. You must have a valid PRCI account to access these links on the PRCI website. It is recommended that when you log into PRCI's website, you enable the 'keep me logged in' feature. Not all the links will be assessable to non-PRCI members.
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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize its performance. This research studies the impact of applying normalization techniques as a pre-processing step to learning, as used by the IDSs. The impacts of pre-processing techniques play an important role in training neural networks to optimize its performance. This report proposes a Deep Neural Network (DNN) model with two hidden layers for IDS architecture and compares two commonly used normalization pre-processing techniques. Our findings are evaluated using accuracy, Area Under Curve (AUC), Receiver Operator Characteristic (ROC), F-1 Score, and loss. The experimentations demonstrate that Z-Score outperforms no-normalization and the use of Min-Max normalization.
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Massari, J. R. License Application Design Selection Feature Report: Additives and Fillers Design Feature 19. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/762917.

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Sisto, A., and C. Kamath. Ensemble Feature Selection in Scientific Data Analysis. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1097710.

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Seo, Young-Woo, Anupriya Ankolekar, and Katia Sycara. Feature Selection for Extracting Semantically Rich Words. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada597268.

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Plinski, M. J. License Application Design Selection Feature Report:Ceramic Coatings. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/762894.

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Tang, J. S. License Application Design Selection Feature Report: Waste Package Self Shielding Design Feature 13. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/752783.

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