distance

examples/classification/distance_based.ipynb

@@ -21,8 +21,7 @@
     " Distance functions have been mostly used with a nearest neighbour (NN) classifier,\n",
     " but you can use them with  [sklearn and aeon distances](../distances/sklearn_distances.ipynb)\n",
     "\n",
-    "<img src=\"./img/dtw2.png\" width=\"400\" alt=\"Example of warping two series to the best\n",
-    "alignment.\">\n",
+    "<img src=\"./img/dtw2.png\" width=\"400\">\n",
     "\n"
    ]
   },
@@ -234,7 +233,8 @@
     "The first algorithm to significantly out perform 1-NN DTW on the UCR data was the\n",
     "Elastic Ensemble (EE) [1]. EE is a weighted ensemble of 11 1-NN classifiers with a\n",
     "range of elastic distance measures. It was the best performing distance based\n",
-    "classifier in the bake off. Elastic distances can be slow, and EE requires cross\n",
+    "classifier in the original bake off [3]. Elastic distances can be slow, and EE requires\n",
+    "cross\n",
     "validation to find the weights of each classifier in the ensemble. You can configure\n",
     "EE to use specified distance functions, and tell it how much."
    ]
@@ -294,8 +294,8 @@
     "### Proximity Forest\n",
     "\n",
     "Proximity Forest [2] is a distance-based ensemble of decision trees.\n",
-    "It is the current state-of-the-art distance-based classifier that\n",
-    "creates an ensemble of decision trees, where the splits are based\n",
+    "It was the best performing algorithm in the 2024 bakeoff [4].\n",
+    "It creates an ensemble of decision trees, where the splits are based\n",
     "on the similarity between time series measured using various\n",
     "parameterised distance measures. The current algorithm is\n",
     "implemented to work for univariate, equal-length time-series data."
@@ -334,8 +334,7 @@
    },
    "source": [
     "## Performance on the UCR univariate datasets\n",
-    "You can find the dictionary based classifiers as follows. Note we do not have a\n",
-    "Proximity Forest implementation in aeon yet, but we do have the results"
+    "You can find the dictionary based classifiers as follows."
    ]
   },
   {
@@ -482,12 +481,19 @@
    "source": [
     "## References\n",
     "\n",
-    "[1] Lines J, Bagnall A (2015) Time series classification with ensembles of elastic\n",
-    "distance measures. Data Mining and Knowledge Discovery 29:565–592\n",
+    "[1] Lines J and Bagnall A (2015) Time series classification with ensembles of elastic\n",
+    "distance measures. Data Mining and Knowledge Discovery 29:565–592 https://link.springer.com/article/10.1007/s10618-014-0361-2\n",
     "\n",
     "[2] Lucas et al. (2019) Proximity Forest: an effective and scalable distance-based\n",
     "classifier. Data Mining and Knowledge Discovery 33: 607--635 https://arxiv.org/abs/1808.10594\n",
-    "\n"
+    "\n",
+    "[3] Bagnall et al. (2017) The great time series classification bake off: a review and\n",
+    "experimental\n",
+    "evaluation of recent algorithmic advances\n",
+    "Data mining and knowledge discovery 31: https://link.springer.com/article/10.1007/S10618-016-0483-9\n",
+    "\n",
+    "[4] Middlehurst et al. (2024) Bake off redux: a review and experimental evaluation of\n",
+    " recent time series classification algorithms. Data mining and knowledge discovery 38: https://link.springer.com/article/10.1007/s10618-024-01022-1"
    ]
   }
  ],