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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Knowledge Enhanced Machine Learning: Techniques & Types

Parth Shukla
Parth Shukla Last Updated : 30 Dec, 2022
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction

In machine learning, the data is an essential part of the training of machine learning algorithms. The amount of data and the data quality highly affect the results from the machine learning algorithms. Almost all machine learning algorithms are data dependent, and their performance can be enhanced until some threshold amount of the data. However, the traditional machine learning algorithm’s behavior tends to be constant after some data is fed to the model.

This article will discuss the knowledge-enhanced machine learning techniques that introduce hierarchical and symbolic methods with limited data. Here we will discuss these methods, their relevance, and working mechanisms, followed by other vital discussions related to them. These methods are proper when there is little data and a need to train an accurate machine-learning model. the article will help one to understand the concepts related to knowledge and enhance machine learning better, and will able to make efficient choices and decisions in case of limited data scenarios.

Knowledge Enhanced Machine Learning

As the name suggests, knowledge enhanced machine learning is a type of technique where the knowledge of machine learning algorithms is enhanced by human capabilities or human understanding. In this technique, the machine learning algorithms apply their knowledge, and human or domain knowledge is integrated.

We humans can be trained on limited data, meaning that humans can learn several things by seeing or practicing stuff quickly and with limited data. For example, If we see a particular device, let’s say a Laptop, we can easily classify it and say it’s a type of electronic device. Also, we can classify it as an HP, Dell, or another model.

knowledge enhanced machine learning

Source – Google

Machine learning models can classify several objects and perform specific tasks very quickly and efficiently, but the only problem is the amount of data. Yes, it requires a lot more amount of data to train an accurate model. But the knowledge-enhanced machine learning approach comes into the picture; it combines majorly two fields, the first is the model’s knowledge, and the other is human knowledge or human capabilities.

Hierarchical Learning and Symbolic Methods are knowledge enhancement machine learning approaches where human knowledge can be used to train a machine learning model with limited data, and the model’s performance can be enhanced.

Hierarchical Learning

As discussed above, when we humans see particular objects, our human mind automatically tries to classify the object into several classes. Let’s try to understand the same thing by taking appropriate examples.

knowledge enhanced machine learning

Source – Google

As discussed above, the human mind can be taken as a trained machine learning model on limited data that classifies the object as a spot into several categories. Let’s take an example to understand the same thing.

Let’s suppose you saw the dog. Looking at the dog, we can easily classify its parent category as a “pet” and classify the dog as a labrador, dalmatian, french bulldog, or poodle. Here we can see that there are several levels of hierarchy where every single layer has several categories, and based on the knowledge of hierarchy, we humans can classify objects.

To implement this approach, the machine learning model can be trained on every layer of the hierarchy, and the model can be hyper-tuned to obtain the hierarchical learning model.

Symbolic Methods

The symbolic methods are also a knowledge base machine learning approach that tries to integrate human knowledge to classify several objects and build an accurate machine learning model.

Some machine learning models are trained so that whenever they are given an unseen image or object, they can efficiently and accurately classify the particular thing. These models are trained on a large amount of data.

We implement the same thing in symbolic methods but with limited data. Here we create the description or tags for the various objects and feed the data to the model. As there is little data available, there will be few images to train the model on, but the description of many objects will still be available.

knowledge enhanced machine learning

Source – Google

Once the model is trained on such data, it can efficiently classify the unseen objects without training in such an image dataset, as it will use the description or tags provided based on human knowledge. So here, human knowledge is used to create the descriptions or tags of several objects, and machine learning models are used to train on such data.

Hierarchical vs. Symbolic Methods

As both approaches use human knowledge, a gentle question might come to mind: What is the main difference between such techniques?

The hierarchical learning approach is more towards a hierarchy of an object and its classification. Here human knowledge is used to classify and create the hierarchy of an object. Then machine learning models are used to train the algorithm on every level of the hierarchy for limited data scenarios.

In Symbolic methods, human knowledge is used to create the descriptions or the tags for particular objects, where the machine learning models are trained on limited image data. This machine learning model can now perform classification tasks on unseen images using human-generated descriptions or tags.

In general, we can not say that one of the approaches is always better, it all depends on the specific scenario of the data models and problem statement. Both approaches are nowadays being used for better performance on limited data, but one could use a specific approach per the requirement and conditions associated.

Conclusion

This article discussed knowledge enhanced machine learning techniques and their types. The hierarchical and symbolic approaches are discussed in detail with their core intuition and the difference between such methods. These articles will help geeks to understand the limited data scenario better and in an efficient way. They will help in several interviews and examinations as it is more of an academic topic.

Some Key Takeaways from this article are:

1. Knowledge-enhanced machine learning is a technique where human knowledge is used to train a machine learning model.

2. In the hierarchical technique, the machine learning model is strained on every hierarchy level generated by human knowledge or domain experts.

3. Symbolic methods also use human knowledge to generate descriptions or tags related to several objects so that the machine learning model can also classify unseen images and objects.

4. Both of the approaches are useful for specific cases and can be implemented as per the requirement and problem statement related to machine learning.

Want to Contact the Author?

Follow Parth Shukla @AnalyticsVidhya, LinkedIn, Twitter, and Medium for more content.

Contact Parth Shukla @Parth Shukla | Portfolio or Parth Shukla | Email to contact me.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

AWS machine learningblogathonHierarchical Clusteringhierarchical modelsknowledge enhanced machine learningsymbolic clustering
Parth Shukla
Parth Shukla

UG (PE) @PDEU | 50+ Published Articles on Data Science | Technical Writer (AI/ML/DL) | Data Science Freelancer | Amazon ML Summer School '22 | Reach Out @[email protected], @portfolio.parthshukla.live

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