Search Bot: Search Intention Based Filtering Using Decision Tree Based Technique

Gupta, Varun; Garg, Neeraj; Gupta, Tarun

doi:10.1109/isms.2012.78

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…[ 34 ] The DT is constructed using the top‐down greedy search approach for the given sets to test each feature at every tree node. [ 30 ] Information gain and entropy are the critical metrics for the evaluation of the DT for classification problems. [ 49 ] By splitting the data into several binary sets, the DT expands.…”

Section: Methodsmentioning

confidence: 99%

“…DT is a supervised machine learning method, which is defined for the classification and regression of problems and can be used for both categorical and continuous variables. [30] In case of overfitting in DTs, the cost complexity post pruning is performed to overcome overfitting. In case of cost complexity post-pruning in the DT method, the unwanted tree nodes are removed to achieve low bias and low variance.…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

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Prediction of bio‐oil yield during pyrolysis of lignocellulosic biomass using machine learning algorithms

2022

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This work aims to implement and use machine learning algorithms to predict the yield of bio-oil during the pyrolysis of lignocellulosic biomass based on the physicochemical properties and composition of the biomass feed and pyrolysis conditions. The biomass pyrolysis process is influenced by different process parameters, such as pyrolysis temperature, heating rate, composition of biomass, and purge gas flow rate. The inter-relation between the yield of different pyrolysis products and process parameters can be well predicted by using different machine learning algorithms. In this study, different machine learning algorithms, namely, multi-linear regression, gradient boosting, random forest, and decision tree, have been trained on the dataset and the models are compared to identify the optimum method for the determination of bio-oil yield prediction model. Analysis of the results showed the gradient boosting method to possess a regression score of 0.97 and 0.89 for the training and testing sets with root-mean-squared error (RMSE) values of 1.19 and 2.39, respectively, and overcome the problem of overfitting. Therefore, the present study provides an approach to train a generalized machine learning model, which can be employed on large datasets while avoiding the error of overfitting.

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Section: Methodsmentioning

confidence: 99%

Section: Decision Tree (Dt)mentioning

confidence: 99%

Prediction of bio‐oil yield during pyrolysis of lignocellulosic biomass using machine learning algorithms

2022

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“…Dalam umpan balik eksplisit, pengguna secara eksplisit memberikan preferensinya sehingga lebih akurat daripada umpan balik implisit. Pada [3] dan [7] pengguna melatih sistem, sehingga umpan balik eksplisit dari pekerjaan mereka digunakan. Kehilangan umpan balik eksplisit adalah bahwa pengguna harus melatih sistem, sehingga ada waktu overhead pelatihan tetapi sistem menebusnya dengan menghemat waktu dan usaha yang lebih besar karena akurasi yang tinggi dari hasil yang difilter.…”

Section: Kesimpulanunclassified

Analisis Feature dan Machine Learning Untuk Pencarian Web

Cipta¹

2018

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The use of website socialization is increasingly still for that amount of information growing rapidly, the harder it is to find the information within the specified time. The main searches for searches that are irrelevant or incompatible with user preferences, keyword-based searches, short questions. In this paper, we will examine the various features used in information retrieval. We will also discuss the relevance of web pages with users. We have classified by feature. In the end, we will compare different techniques and their pros and cons are discussed.

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