Semi Supervised Classification

Last Updated : 7 Apr, 2026

Semi supervised classification is a technique in data mining that uses both labeled and unlabeled data to build a classification model. Usually, only a small portion of the dataset has labels, while the remaining data is unlabeled. The model learns patterns from both types of data to improve classification performance.

semi_supervised_classification_in_data_mining
Semi-Supervised Classification
  • Uses both labeled and unlabeled data.
  • Only a small portion of data is labeled.
  • Most data remains unlabeled.
  • Useful when labeling data is difficult or costly.
  • Combines supervised and unsupervised learning

Working

working_of_semi_supervised_classification_in_data_mining
Working of Semi-Supervised Classification

The working of semi supervised classification in data mining can be explained in the following steps:

1. Collect Labeled and Unlabeled Data: Gather a dataset that contains a small amount of labeled data and a large amount of unlabeled data.

Example: A few images of animals with labels and many images without labels.

2. Choose a Classification Algorithm: Select a suitable classification algorithm that can work with both labeled and unlabeled data.

3. Train the Model with Labeled Data: The model first learns patterns and relationships using the available labeled data.

4. Use Unlabeled Data for Learning: The model then uses the unlabeled data to discover additional patterns and improve its understanding.

5. Improve Classification Performance: By combining both types of data, the model becomes better at classifying new or unseen data.

Implementation

Step 1: Import Required Libraries

Import the necessary Python libraries for data handling, machine learning and evaluation.

Python 
import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.semi_supervised import LabelPropagation
from sklearn.metrics import accuracy_score, confusion_matrix

Step 2: Load the Dataset

  • The Iris dataset is loaded from sklearn.
  • It contains 150 samples and 3 flower classes.
  • Features represent measurements of petals and sepals.
Python 
data = load_iris()

X = data.data
y = data.target

df = pd.DataFrame(X, columns=data.feature_names)
df["target"] = y

df.head()

Output:

output10
Dataframe

Step 3: Visualize the Dataset

This visualization shows relationships between features.

  • Each color represents a different class.
  • It helps understand how the classes are distributed.
  • Good separation between classes makes classification easier.
Python 
sns.pairplot(df, hue="target")
plt.show()

Output:

output2
Pairplot to visualize relation

Step 4: Split the Dataset

  • 70% of data is used for training.
  • 30% of data is used for testing.
  • The model learns from training data and is evaluated on test data.
Python 
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)

Step 5: Create Unlabeled Data

Semi supervised learning assumes that most data is unlabeled.

  • Some training labels are removed.
  • -1 represents unlabeled samples.
  • The model will try to infer these labels during training.
Python 
y_train_semi = y_train.copy()

rng = np.random.RandomState(42)
random_unlabeled = rng.rand(len(y_train_semi)) < 0.7

y_train_semi[random_unlabeled] = -1

Step 6: Visualize Labeled vs Unlabeled Data

This plot shows the distribution of labeled and unlabeled samples.

  • Colored points represent labeled data.
  • Dark points represent unlabeled data.
  • This demonstrates the semi-supervised setting.
Python 
plt.figure(figsize=(6,4))

plt.scatter(X_train[:,0], X_train[:,1], c=y_train_semi, cmap="viridis")

plt.title("Labeled vs Unlabeled Data")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")

plt.show()

Output:

output3
Labeled vs Unlabeled data

Step 7: Train the Semi Supervised Model

The model learns patterns using:

  • Available labeled data
  • Structure of unlabeled data
Python 
model = LabelPropagation()

model.fit(X_train, y_train_semi)

Step 8: Predict the Test Data

The trained model predicts the class of unseen samples.

Python 
predictions = model.predict(X_test)

Step 9: Evaluate Model Accuracy

  • Accuracy measures how many predictions are correct.
  • Higher accuracy means the classification model performs better.
Python 
accuracy = accuracy_score(y_test, predictions)

print("Accuracy:", accuracy)

Output:

Accuracy: 1.0

Step 10: Confusion Matrix Visualization

The confusion matrix shows:

  • Correct predictions along the diagonal
  • Misclassifications in other cells
Python 
cm = confusion_matrix(y_test, predictions)

sns.heatmap(cm, annot=True, cmap="Blues")

plt.title("Confusion Matrix")
plt.xlabel("Predicted Label")
plt.ylabel("True Label")

plt.show()

Output:

output4
Confusion Matrix

Download full code from here

Applications

  • Used in audio data to improve speech recognition systems.
  • Since billions of web pages exist, labeling them manually is not practical. It helps automatically classify and organize web content.
  • Uses a few labeled texts and many unlabeled ones to classify documents into categories.

Advantages

  • Semi supervised classification is relatively simple to understand and implement because it combines ideas from supervised and unsupervised learning.
  • Requires only a small amount of labeled data, which reduces the effort and cost involved in manually labeling large datasets.
  • Algorithm can still learn useful patterns by using a large amount of unlabeled data along with the available labeled samples.

Disadvantages

  • Results of the algorithm may change across different iterations because the model continuously updates labels during training.
  • May not effectively capture complex relationships present in large network or graph based datasets.
  • Accuracy may not always be high, especially when the unlabeled data contains noise or misleading patterns. 
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